SPECIAL EDUCATION RESEARCH, POLICY & PRACTICE

SPECIAL EDUCATION RESEARCH, POLICY & PRACTICE (2022 Edition)

2022 Edition Volume 6 Table of Contents

Page

Editorial Board of Reviewers

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Using the Development of a Mobile Application to Teach Health Behaviors through Social Stories to Individuals with Autism Samantha Casadonte, Julia VanderMolen, and Terri Gustofson

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Effects of State-Level Funding Systems on Identification Rates of Students with Learning Disabilities Brad Uhing and Michelle Powers

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Stress-Management Interventions for Special Education Teachers: A Systematic Literature Review Haya Abdellatif and Rachel Robertson

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Systematic Barriers to Voting for Adults with Disabilities Alena Bates, Holly Collinsworth, and Cara Speicher

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Familiar vs. Unfamiliar Stimuli in Multiple Stimulus Preference Assessments Samantha Didrichsen and Mary. E. McDonald

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Community Factors and High School Services for Diploma-Track 78 Students on the Autism Spectrum Jade LaRochelle, Elizabeth G. S. Munsell, Elizabeth K. Schmidt, Gael Orsmond, and Wendy Coster 1

Teaching General Education Preservice Teachers Special Education Acronyms using SAFMEDS Renée E. Lastrapes

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General Education and Special Education Teacher Perspectives on Integrated Co-Teaching Jordan McCaw

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Using Behavioral Momentum to Increase Compliance in a Preschooler with Autism Beth Pergament and Mary E. McDonald

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A Model for Conducting a Brief Experimental Analysis to Assess a Student’s Ability to Perform a Replacement Behavior Linda M. Reeves and Abigail Baxter

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The Value of a Collaborative Approach to Addressing Executive Functioning Weakness in School-Based Intervention: An Occupational Therapy Perspective Michele M. Stillman, Allison Sullivan, and Chris Alterio

154

Author Guidelines

171

Publishing Process

172

Copyright and Reprint Rights

173

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Editorial Board of Reviewers All members of the Hofstra University Special Education Department will sit on the Editorial Board for the SPECIAL EDUCATION RESEARCH, POLICY & PRACTICE. Each of the faculty will reach out to professionals in the field whom he/she knows to start the process of building a list of peer reviewers for specific types of articles. Reviewer selection is critical to the publication process, and we will base our choice on many factors, including expertise, reputation, specific recommendations and previous experience of a reviewer. Editor George Giuliani, J.D., Psy.D., Hofstra University Hofstra University Special Education Faculty Elfreda Blue, Ph.D. Stephen Hernandez, Ed.D. Gloria Lodato Wilson, Ph.D. Mary E. McDonald, Ph.D, BCBA-D, LBA Darra Pace, Ed.D. Diane Schwartz, Ed.D. Editorial Board Mohammed Alzyoudi, Ph.D., American University in the Emirates. Dubai. UAE Faith Andreasen, Ph.D. Vance L. Austin, Ph.D., Manhattanville College Amy Ballin, Ph.D. Dana Battaglia, Ph.D., CCC-SLP, Westbury UFSD Brooke Blanks, Ph.D., Radford University Kathleen Boothe, Ph.D., Southeastern Oklahoma State University Nicholas Catania, PhD, State College of Florida, Manatee-Sarasota Lindsey A. Chapman, Ph.D., University of Florida Morgan Chitiyo, Ph.D., University of North Carolina at Greensboro Jonathan Chitiyo, Ph.D., University of Pittsburgh at Bradford Heidi Cornell, Ph.D., Wichita State University Lesley Craig-Unkefer, Ed.D., Middle Tennessee State University Amy Davies Lackey, Ph.D., BCBA-D Lauren Dean, Ed.D., Hofstra University Josh Del Viscovo, MS, BCSE, Northcentral University Darlene Desbrow, Ph.D., United States University Janet R. DeSimone, Ed.D., Lehman College, The City University of New York Lisa Dille, Ed.D., BCBA, Georgian Court University William Dorfman, B.A. (MA in progress), Florida International University Brandi Eley, Ph.D. Tracey Falardeau M.A., M.S., Midland Educational Agency Danielle Feeney, Ph.D., Ohio University Lisa Fleisher, Ph.D., New York University 3

Neil O. Friesland, Ed.D., MidAmerica Nazarene University Theresa Garfield, Ed.D., Texas A&M University-San Antonio Leigh Gates, Ed.D., University of North Carolina Wilmington Sean Green, Ph.D. Deborah W. Hartman, M.S., Cedar Crest College Shawnna Helf, Ph.D., Winthrop University Nicole Irish, Ed.D., University of the Cumberlands Randa G. Keeley, PhD, Texas Woman's University Hyun Uk Kim, Ph.D., Eastern Oregon University Louisa Kramer-Vida, Ed.D., Long Island University Nai-Cheng Kuo, PhD., BCBA, Augusta University Renée E. Lastrapes, Ph.D., University of Houston-Clear Lake Debra Leach, Ed.D., BCBA, Winthrop University Marla J. Lohmann, Ph.D., Colorado Christian University Mary Lombardo-Graves, Ed.D., University of Evansville Pamela E. Lowry, Ed.D., Georgian Court University Denise Lucas, M.S. Matthew D. Lucas, Ed.D., Longwood University Jay R. Lucker, Ed.D., Howard University Jennifer N. Mahdavi, Ph.D., BCBA-D, Sonoma State University Alyson Martin, Ed.D., Fairfield University Krystle E. Merry, M.S. Ed., NBCT., Ph.D. Student, University of Arkansas Marcia Montague, Ph.D., Texas A&M University Chelsea T. Morris, Ph.D., University of West Georgia Gena Nelson, Ph.D., University of Oregon Lawrence Nhemachena, MSc, Universidade Catolica de Mozambique Maria B. Peterson Ahmad, Ph.D., Western Oregon University Christine Powell. Ed.D., California Lutheran University Deborah Reed, Ph.D., University of Tennessee Ken Reimer, Ph.D., University of Winnipeg Dana Reinecke, PhD, BCBA-D, Capella University Denise Rich-Gross, Ph.D., University of Akron Benjamin Riden, ABD - Ph.D., Penn State Mary Runo, Ph.D., Kenyatta University Emily Smith, Ed.D., Midwestern State University Carrie Semmelroth, Ed.D.., Boise State University Pamela Mary Schmidt, M.S., Freeport High School Special Education Department Edward Schultz, Ph.D., Midwestern State University Mustafa Serdar Köksal, Ph.D., Hacettepe University, Turkey Emily R. Shamash, Ed.D., Fairfield University Christopher E. Smith, PhD, BCBA-D, Positive Behavior Support Consulting & Psychological Resources Emily Smith, Ed.D., Midwestern State University Gregory W. Smith. Ph.D., University of Southern Mississippi Emily Sobeck, Ph.D., Franciscan University Ernest Solar, Ph.D., Mount St. Mary’s University 4

Gretchen L. Stewart , Ph.D., University of South Florida Roben Taylor Daubler, Ed.D., Western Governors University Jessie Sue Thacker-King, Flagler College Julia VanderMolen, Ph.D., Grand Valley State University Cindy Widner, Ed.D., Carson Newman University Kathleen G. Winterman, Ed.D., Xavier University Sara B. Woolf, Ed.D., Queens College, City University of New York Perry A. Zirkel, J.D., Ph.D., Lehigh University

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Using the Development of a Mobile Application to Teach Health Behaviors through Social Stories to Individuals with Autism Samantha Casadonte, MPH Julia VanderMolen, Ph.D., CHES Grand Valley State University Terri Gustofson, Ph.D Northwestern Michigan College Abstract Autism spectrum disorder (ASD), a neurodevelopmental disorder typically diagnosed in childhood, is characterized by communication deficits, impaired language, and difficulties with social situations (American Psychiatric Association 2013). Social stories are a strategy for developing social understanding in children with ASD. This research aimed to evaluate training materials to help teachers create a mobile application to help children with ASD and their social behaviors. A survey was sent on how to use Glide to develop simple social stories. The survey results revealed that five respondents indicated that Glide was easy to use (n = 5, 71.4%), and 100% (n = 7) of the respondents said that the development of an individualized mobile application could align with IEP goals. This study provides pilot information regarding the development of individualized mobile applications to create social stories. Future research should continue to examine the role of bibliotherapy and assistive technology as a learning tool. Introduction Autism spectrum disorder (ASD), is a neurodevelopmental disorder that affects 1 in 54 individuals in the United States. ASD can be characterized by difficulty with understanding social contexts, difficulty understanding nonverbal communication, as well as challenges in maintaining social relationships (American Psychiatric Association 2013). Children with autism spectrum disorder also have deficits in social-emotional language and communication skills and often spend more time playing alone than their peers (Koegel et al., 2001; Shabani et al., 2002; Valentini, S. & Robertson, 2019).Individuals with autism spectrum disorder (ASD) frequently experience challenges that impact their ability to understand and navigate a world designed for people without disabilities (CDC, 2020). Dependency in daily activities such as personal hygiene, healthy eating, and social situations may create poor social and vocational outcomes (Wertalik & Kubina, 2017). Individuals with ASD often require different methods of learning, which are different from typical academic methods. Social stories are a resource that educators and parents have found to enhance social education for children and adults with ASD. Social stories are resources that can be accessed through physical creation, print books and media, online books and resources, and recently through mobile applications. The purpose of the study was to evaluate knowledge and basic training materials of the Glide mobile application developer to help teach health behaviors to individuals with autism spectrum disorder through social stories.

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Social Stories Social Stories™ were first developed by Carol Gray in 1991 as a strategy for developing social understanding in children with ASD. A social story is "...a short story that adheres to a specific format and guidelines to objectively describe a person, skill, event, concept, or social situation... The goal of a social story is to share relevant information. The information often includes where and when a situation takes place, who is involved, what is occurring, and why” (Gray, 1998, p. 171). Moreover, social stories are simple, short, personalized narratives composed of various sentence types which describe or help teach an individual a specific behavior (Gray, 2010). Social stories as a teaching method are frequently used for children with ASD to understand social skills (Samuels and Stansfield, 2012). Social stories can be presented in multiple ways, including mobile applications, books, storyboards, and more (Ghanouni et al., 2018). Social stories aim to explain certain concepts, interactions, and situations that may confuse an individual with ASD (Samuels and Stansfield, 2012). Specifically, Wong et al. (2014) found that social stories effectively addressed social, communication, behavior, joint attention, play, schoolreadiness, academic, and adaptive skills. Furthermore, Hanrahan et al. (2020) provide research indicating that maladaptive social behaviors may have adverse health effects on individuals with autism through anxiety levels and communication barriers. The study results concluded that using social stories effectively produced beneficial behavioral outcomes changes to intervene in maladaptive behavior (Hanrahan et al., 2020). Social stories can be used on multiple different media platforms. Researchers have hypothesized that the combination of social stories and virtual learning may have positive outcomes on learning for people with ASD. Ghanouni et al. (2018) conducted a study using a virtual reality system comprising 75 social stories. The presentation of social stories in a virtual setting may increase motivation for participation in learning for children with an autism spectrum disorder. A study from Volioti et al. (2016) indicated that a combination of pictures or presenting social stories in virtual learning may facilitate communication among children with an autism spectrum disorder. Volioti et al. (2016) also outline virtual learning environments' success in overcoming social communication and imagination differences. Health Behaviors Characteristics of ASD may influence lifestyle habits and health behaviors (Healy et al. 2017). On average, youth with ASD tend to have poorer lifestyle habits compared to their typically developing (TD) counterparts (Healy et al. 2017). Adolescents with ASD tend to participate in fewer minutes per day of physical activity (Garcia & Hans-Vaughn, 2021; Healy et al. 2017). The lack of health-related behaviors has been linked with a greater prevalence of obesity and obesity-related diseases in this population (Garcia & Hans-Vaughn, 2021; Kahathuduwa et al. 2019). The use of social stores as an intervention cna provide social information in a simple visual format that explains what to expect and what constitutes appropriate behavior which can include health behavior (Smith et al., 2013). The Use of Mobile Applications in Intervention Research indicates that technological learning may be an effective tool in teaching individuals with ASD. Interventions using technology systems for people with ASD, such as computers and tablets, have made considerable advances in recent times, making them more readily accessible for families. Individuals with ASD often have a high affinity for these devices (Parsons et al., 7

2019.) Beckman et al. (2019) provide research on how mobile applications improve behavioral and academic outcomes for students with an autism spectrum disorder. The study results indicate a functional relation between mobile application implementation and on-task behavior from baseline to intervention phases. Mcquiggan, George, & Morten (2015) explain how an iPad ™ can be used to acquire learning for individuals with an autism spectrum disorder. The book offers insight into mobile devices' use in creating an equitable learning environment and a communication tool for individuals with an autism spectrum disorder. The iPad as an educational tool allows individuals with autism who may not use verbal language to communicate and engage in classroom learning environments (Mcquiggan, George, & Morten, 2015). The Glide App Glide is a website that can pull data directly from Google Sheets to create an app that can run on iOS, Android devices, Windows and is also web-based. There is no coding required to build the app; students simply need a well-organized spreadsheet and a sense of design for the user interface (Denby, 2019). Teachers can take an existing Google Sheet or create one from scratch and then use Glide to design a fully functional app interface. The app can then be shared via QR code, link, or even phone number. The app responds to the input of different users, updating the original spreadsheet and the app in real-time (Denby, 2019). Figure 1 is a basic screenshot illustrating an essential handwashing Google Sheet through the Glide app. Figure 2 is a screenshot showing the basic handwashing template example. Figure 3 illustrates the practical use of a handwashing template in Glide.

Figure 1. Screenshot of a Google Sheet through the Glide app Note. The Google sheet provides plain language and images for the basic steps to handwashing.

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Figure 2. Screenshot of Handwashing Glide Template Note. Glide can be used to create flashcards, checklists, picture cards, calendars, and more. Pictures can be personal photographs or uploaded from the internet.

Figure 3. Screenshot of Step 6. Rinse hands underwater Note: This figure shows an example view of the handwashing in the final application created using Glide.

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Methods Purpose of Study The purpose of the study was to evaluate the knowledge and basic training materials of a mobile application developer to help teach health behaviors to individuals with autism spectrum disorder through social stories. This study was approved for human subjects review (21-181-H). Participants A convenience sample was used to recruit participants. Participants were educators who attended and participated in a poster presentation during the teaching and learning with technology conference in March of 2021. Participants included elementary teachers, middle school teachers, high school teachers, and faculty from higher education. Inclusion criteria include individuals who signed up to attend the conference. The target goal for the poster presentation was 20 attendees. Seven participants completed the survey for the study. Instrument and Data Collection Participants were recruited during a 45-minute conference poster presentation who participated in mobile application development training for developing individualized social stories for individuals with ASD. Participants were provided a survey designed using Qualtrics. The survey questions were adapted from the App Checklist for Educators (ACE) by Lubniewski, McArthur & Harriott (2017) and a survey from Ventimiglia (2007). The ACE checklist consisted of five sections which include student interest in a mobile application, design features, connection to curriculum, instruction features and overall rating and review. Ordinal data were gathered in a Likert scale with the values Yes, No, Somewhat, N/a. The survey consists of fifteen questions (n = 15) with a completion time of less than ten minutes. Of the 15 questions, 10 questions were adapted from the ACE Checklist. The questions from the ACE Checklist included Can the mobile application align with IEP goals? Is the mobile application easy to use? Will students find this app interesting? Is the layout clear and consistent? Is content prepared in a culturally inclusive manner? Can content match with student skill level? Can it be applied to real world situations? Will it improve students' skills? Q26 Would you recommend this app to other professionals? Would you recommend this app to families? Two questions addressed social stories, followed by two demographic questions. Finally, space for feedback concerning difficulties completing the survey or suggestions was included but optional. All responses in the survey were anonymous, and no identifiable information was collected. Data Analysis To ensure that confidentiality was maintained for all participants, the collection of personal information, such as names and addresses, was not required for the survey. Demographic information of participants was collected. The data was collected through Qualtrics, and participants remained anonymous by using the Qualtrics anonymous link feature. Data were analyzed using Statistical Software for the Social Sciences (SPSS) Version 26. Descriptive statistics were used to support the research question. Descriptive statistics summarize statistics that quantitatively describe the basic features of the data from the survey.

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Results A survey regarding a mobile application to teach health behaviors was administered to individuals who attended the poster presentation training for the mobile application at a teaching and learning with technology conference. Eleven respondents started the survey, and seven respondents (n = 7) completed the survey. The participant demographics are provided in Table 1. Table 1 Demographics Characteristic of Participants Characteristic Grade Levelsa

n

%

Elementary

3

50.00%

Middle Schools

0

0.00%

High School

1

16.67%

Other:

2

33.33%

Special Education Teacher

5

83.33%

Speech-Language Pathologist

0

0.00%

Behavior Therapist

0

0.00%

Occupational Therapist

0

0.00%

Teacher Aide or Paraprofessional

0

0.00%

General Education

0

0.00%

Psychologist

0

0.00%

Other profession not listed

1

16.67%

n

%

Autism Spectrum Disorder (ASD)

6

23.08%

Cognitive Impairment (CI)

6

23.08%

Deaf or Hard of Hearing (DHH)

1

3.85%

Job Titlesa

Student Population

11

Early Childhood Developmental Delay

3

11.54%

Emotional Impairment

2

7.69%

Specific Learning Disability

0

0.00%

Traumatic Brain Injury

3

11.54%

Speech and Language Impairment

4

15.38%

Population not listed:

1

3.85%

Note. N = 7. Only six participants completed the demographic questions. a

Reflects the number and percentage of participants answering the questions.

App Checklist for Educators Analysis The results from the App Checklist for Educators (ACE) indicate that 100% (n = 7) of the respondents said that the development of an individualized mobile application could align with IEP goals. Additionally, all of the respondents indicated that an individualized mobile application would engage a student’s interest. A clear and consistent layout can match students with different skill levels and can be applied to real-world situations. Furthermore, 100% (n =7) of the respondents also indicated that they would suggest the mobile application to other professionals and parents. When asked if the application was prepared in a culturally inclusive manner, six participants responded that the mobile application is prepared in a culturally inclusive manner. One respondent marked that the question was not applicable (N/a). Moreover, when asked if the mobile application was easy to use, five respondents indicated that the mobile application is easy to use (n = 5, 71.4%). In comparison, two respondents marked that the application is somewhat easy to use (n = 2, 26.6%). Figure 4 shows the responses addressing the ease of use.

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Figure 4. Ease of using Glide to create individual mobile applications. Note. This figure shows the response rate addressing the ease of use of creating mobile applications using Glide. Seven participants responded (M = 1.567; SD = 0.90). Social story and health behavior intervention The survey contains two multiple-choice questions regarding the use of social stories in health behavior intervention. The survey asks respondents which population of students they have found social stories to be most effective. The results indicated 54.4% (n = 6) for ASD, 54.4% (n = 6) for cognitive impairment (CI), 9.1% (n = 1) for Deaf or hard of hearing, 27.3% (n = 3) for early childhood delay, 18.28% (n = 2) for emotional delay, 27.3% (n = 3) for traumatic brain injury (TBI), 36.4% (n = 3) for speech or language impairment, and 9.1% (n = 1) for a population not listed. When respondents were asked to indicate situations in which they would use social stories, 36.4% (n = 4) indicated a change in routines, 54.5% (n = 6) indicated following routines, 54.5% indicated dealing with emotions, 36.4% (n = 4) indicated health and wellness, 54.5% (n = 6) indicated personal hygiene or personal care, 27.3% (n = 3) indicated major life events, and 27.3% (n = 3) indicated behavior change. Figure 5 provides the breakdown of how developing and using a mobile application can benefit certain health behaviors of children with ASD.

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Figure 5. Purpose of Using Social Stories Notes. This figure provides preliminary data regarding the specific health behaviors in which an individualized mobile application would benefit an individual with ASD. The X axis represents the typical health behavior that teachers encounter. The Y axis represents the frequency of the behavior. Following a routine, dealing with emotions, and personal care and hygiene provided the most significant number of responses. It is important to note that the question addressing the purpose of using a social story was to check all that applied. Discussion Previous research has shown mixed support for using social story interventions alone to improve social initiations in children with ASD (Valentini & Robertson, 2019, Golzari et al., 2015). The present study aimed to add to the research in this area by investigating whether a social story is provided in a digital or mobile application format. Additionally, the study drew upon the perception from educators and their evaluation of a mobile application used to develop social stories for individuals with ASD. Glide can increase access to social stories, allow for personalization depending on needs, and allow for generalizability of skills, such as handwashing, across multiple settings (i.e., home, school). The study's findings suggest that a social story developed using a mobile application can customize a learning experience for individuals with ASD. All of the teachers indicate the ease and benefit of developing. Furthermore, the survey results suggest that the Glide may effectively teach health behaviors to individuals with ASD and many other diagnoses that require special education services such as cognitive impairments or traumatic brain injury. Further research should investigate the mobile application’s effectiveness in acquiring health behaviors with individuals with ASD. 14

Strengths and Limitations The strengths of this research are the extensive amount of research on autism spectrum disorder that is readily available and the convenience sample. However, this is also a limitation as the study includes the inability to reach the target sample size of 20 survey respondents. Another limitation is the lack of diversity in the sample size, as most of the respondents indicated that they are special education teachers. Lastly, a limitation to this study is the limited amount of previous research regarding health behaviors learned through social stories in conjunction with technology such as an individualized mobile application. Studies have shown that the addition of technology better assists educators in achieving developmental goals related to literacy levels in children with ASD (Mandak et al., 2019). Adding questions about using digital bibliotherapy or social stories in the survey would have created a better understanding of social story use in general. Additionally, it would be an essential step in developing future research to benefit the ASD population. Conclusion The purpose of the study was to evaluate knowledge and basic training materials of the Glide, a mobile application developer, to help teach health behaviors to individuals with ASD through social stories. This research study suggests that teaching educators more extensively on social stories and developing an individualized mobile application for those with ASD and its use is an important step in creating a potential tool for this population. Social Stories and individualized mobile application development can be a helpful tool for children with ASD. By furthering research and education on social stories and individualized mobile application development effects on children with ASD, more children can be exposed to an innovative therapeutic intervention that could assist them with a broad range of behavioral health issues. Proper understanding of social stories and widespread promotion of its benefits is the first step in creating a new health literacy tool for those with ASD. Future research should continue to examine the role of bibliotherapy in which social stories, particularly for children with ASD of varying ages, abilities, and learning styles, are recommended. Social stories assist readers in completing one specific task or scenario (Vandermeer et al., 2015). While social stories are a valuable tool for those with ASD, it is more effective with older children who can read and understand text (Vandermeer et al. 2015). When more research on the effectiveness of social stories is established, research on using social stories to help children with ASD achieve better health literacy skills at all ages can begin (Abraham et al., 2020). References Abraham, S., Owen-De Schryver, J. & VanderMolen, J. (2020). Assessing the effectiveness and use of bibliotherapy implementation among children with autism by board-certified behavior analysts. Journal of Autism and Developmental Disorders. 10.1007/s10803020-04727-6 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association. Beckman, A., Mason, B. A., Wills, H. P., Garrison-Kane, L., & Huffman, J. (2019). Improving behavioral and academic outcomes for students with autism spectrum disorder: Testing

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an app-based self-monitoring intervention. Education and Treatment of Children, 42(2), 225-244. 10.1353/etc.2019.0011 Centers for Disease Control and Prevention. (2020, March 25). What is autism spectrum disorder? https://www.cdc.gov/ncbddd/autism/facts.html Delano, M., & Snell, M. E. (2006). The effects of social stories on the social engagement of children with autism. Journal of Positive Behavior Interventions, 8(1), 29–42. https://doi.org/10.1177/10983007060080010501 Denby, J. (2019). Glide review for teachers. Common Sense Media. https://www.commonsense.org/education/website/glide Garcia, J. M., & Hahs-Vaughn, D. L. (2021). Health factors, sociability, and academic outcomes of typically developing youth and youth with autism spectrum disorder: A latent class analysis approach. Journal of Autism and Developmental Disorders, 51(4), 1346-1352. Ghanouni, P., Jarus, T., Zwicker, J. G., Lucyshyn, J., Mow, K., & Ledingham, A. (2018; 2019). Social Stories for children with autism spectrum disorder: Validating the content of a virtual reality program. Journal of Autism and Developmental Disorders, 49(2), 660-668. 10.1007/s10803-018-3737-0 Golzari, F., Hemati Alamdarloo, G., & Moradi, S. (2015). The effect of a social stories intervention on the social skills of male students with autism spectrum disorder. SAGE Open, 5(4). Gray, C. (2010). The new social storybook. Arlington, Texas: Future Horizons. Hanrahan, R., Smith, E., Johnson, H., Constantin, A., & Brosnan, M. (2020). A pilot randomized control trial of digitally-mediated social stories for children on the autism spectrum. Journal of Autism and Developmental Disorders, 10.1007/s10803-020-04490-8 Healy, S., Haegele, J. A., Grenier, M., & Garcia, J. M. (2017). Physical activity, screen-time behavior, and obesity among 13-year olds in Ireland with and without autism spectrum disorder. Journal of Autism and Developmental Disorders, 47(1), 49–57. Hsu, Y., & Ching, Y. (2013). Mobile app design for teaching and learning: Educators’ experiences in an online graduate course. The International Review of Research in Open and Distributed Learning, 14(4) 10.19173/irrodl.v14i4.1542 Hsu, Y., Rice, K., & Dawley, L. (2012). Empowering educators with Google's Android app inventor: An online workshop in mobile app design. British Journal of Educational Technology, 43(1), E1-E5. 10.1111/j.1467-8535.2011.01241.x Kahathuduwa, C., West, B., Blume, J., Dharavath, N., Moustaid-Moussa, N., & Mastergeorge, A. (2019). The risk of overweight and obesity in children with autism spectrum disorders: A systematic review and meta-analysis. Obesity Reviews. https://doi.org/10.1111/obr.12933. Koegel, L. K., Koegel, R. L., Frea, W. D., & Fredeen, R. M. (2001). Identifying early intervention targets for children with autism in inclusive school settings. Behavior Modification, 25(5), 745–761. https://doi.org/10.1177/0145445501255005 Kurt, O., & Kutlu, M. (2019). Effectiveness of social stories in teaching abduction-prevention skills to children with autism. Journal of Autism and Developmental Disorders, 49(9), 3807-3818. 10.1007/s10803-019-04096 Lieberman, M. (2019, February 27). ‘Students are using mobile even if you aren’t. Inside Higher Ed. Retrieved https://www.insidehighered.com/digitallearning/article/2019/02/27/mobile-devices-transform-classroom-experiences-and Liss, M., Harel, B., Fein, D., Allen, D., Dunn, M., Feinstein, C., Morris, R., Waterhouse, L., & 16

Rapin, I. (2001). Predictors and correlates of adaptive functioning in children with developmental disorders. Journal of autism and developmental disorders, 31(2), 219– 230. https://doi.org/10.1023/a:1010707417274 Lubniewski, K. L., McArthur, C. L., & Harriott, W. (2018). Evaluating instructional apps using the App Checklist for Educators (ACE). International Electronic Journal of Elementary Education, 10(3), 323–329. 10.26822/iejee.2018336190 Mandak, K., Light, J., & McNaughton, D. (2019). Digital books with dynamic text and speech Output: Effects on sight word reading for preschoolers with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 49(3), 1193–1204. https://doi.org/10.1007/s10803-018-3817-1 McArthur, C. L., & Lubniewski, K. L. (2018). Evaluating instructional apps using the app checklist for educators (ACE). International Electronic Journal of Elementary Education, 10(3), 323-329. 10.26822/iejee.2018336190 McQuiggan, S., George, T. K., & Morton, L. (2015). Mobile learning: A handbook for developers, educators, and learners (1st ed.). Hoboken, New Jersey: Wiley. Mitchell, K., Regehr, K., Reaume, J., & Feldman, M. (2010). Group social skills training for adolescents with Asperger syndrome or high functioning autism. Journal on Developmental Disabilities, 16(2), 52–63. 10.1007/s10803-006-0343-3 Rogers, S. J. (2009). What are infant siblings teaching us about autism in infancy? Autism Research, 2(3), 125–137. 10.1002/aur.81 Samuels, R., & Stansfield, J. (2012). The effectiveness of social stories™ to develop social interactions with adults with characteristics of autism spectrum disorder. British Journal of Learning Disabilities, 40(4), 272–285. https://doi.org/10.1111/j.14683156.2011.00706.x Schohl, K. A., Van Hecke, A. V., Carson, A. M., Dolan, B., Karst, J., & Stevens, S. (2013;2014;). A replication and extension of the PEERS intervention: Examining effects on social skills and social anxiety in adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 44(3), 532-545. 10.1007/s10803-013-1900-1 Shabani, D. B., Katz, R. C., Wilder, D. A., Beauchamp, K., Taylor, C. R., & Fischer, K. J. (2002). Increasing social initiations in children with autism: effects of a tactile prompt. Journal of applied behavior analysis, 35(1), 79–83. https://doi.org/10.1901/jaba.2002.3579 Smith, E., Constantin, A., Johnson, H., & Brosnan, M. (2021). Digitally-mediated social stories support children on the Autism Spectrum adapting to a change in a ‘Real-World’context. Journal of Autism and Developmental Disorders, 51, 514-526. Vandermeer, J., Beamish, W., Milford, T., & Lang, W. (2015). iPad-presented social stories for young children with autism. Developmental Neurorehabilitation, 18(2), 75-81. 10.3109/17518423.2013.809811 Valentini, S. & Robertson, R. (2019). Using social stories to increase social initiations by a student with autism to typical peers. Special Education Research, Policy and Practice. 3(1). 37-53. https://issuu.com/hofstra/docs/2019_edition__special_education_research__policy_ Vandermeer, J., Beamish, W., Milford, T., & Lang, W. (2015). iPad-presented social stories for young children with autism. Developmental neurorehabilitation, 18(2), 75–81. https://doi.org/10.3109/17518423.2013.809811

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Ventimiglia, Alana, "The Effects of Social Stories on the Social Interaction and Behavior of Students with Autism Spectrum Disorders" (2007). Education Masters. Paper 271. Wertalik, J. L., Wertalik, J. L., Kubina Jr, R. M., & Kubina Jr, R. M. (2017). Interventions to improve personal care skills for individuals with autism: A review of the literature. Review Journal of Autism and Developmental Disorders, 4(1), 50-60. 10.1007/s40489016-0097-6 Wong, C., Odom, S. L., Hume, K. A., Cox, A. W., Fettig, A., Kucharczyk, S., Brock, M. E., Plavnick, J. B., Fleury, V. P., & Schultz, T. R. (2015). Evidence-based practices for children, youth, and young adults with Autism Spectrum Disorder: A comprehensive review. Journal of Autism and Developmental Disorders, 45(7), 1951–1966. https://doi.org/10.1007/s10803-014-2351-z About the Authors Samantha Casadonte is a recent Masters in Public Health (MPH) graduate from Grand Valley State University. She has presented at numerous conferences addressing disabilities, specifically autism. She has presented at the 2020 and 2021 Michigan Computer Users for Learning Conference and her topic has been presented at the 2021 Michigan Occupational Association (MiOTA) conference. Dr. Julia VanderMolen is an Associate Professor for the Public Health program at Grand Valley State University. The contributions of her research are to examine the benefits of assistive technology, UD and UDL, and disabilities in public health.She is an active member of the Disability section of the American Public Health Association (APHA). Terri Gustofson, Ph.D is the Director of Educational Technology at Northwestern Michigan College. The contributions to her research focuses on online and blended learning design, the adoption and use of technology by students in academic settings , and how it affects faculty success implementing emerging technology in courses.

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Effects of State-Level Funding Systems on Identification Rates of Students with Learning Disabilities Brad Uhing, Ph.D. Michelle Powers, Ed.D. Augustana University Abstract Special education funding formulas vary by state and are designed to fulfill the mandates of IDEA for identification. This is particularly impactful for students with a learning disability (LD) who make up approximately half of all students served in special education. The purpose of this study was to determine if state special education funding formulas were predictive of LD diagnosis and PPS, as well as whether per pupil spending (PPS) correlated with the rate of LD identification. Results found that various funding formulas were not predictive of identification rates or PPS, and correlations were not identified between PPS and LD. States using Multiple Reimbursement Models for funding often identified a higher percentage of students with LD while spending more per pupil. These results may provide a better understanding of state special education funding formulas and is an important consideration for education policymakers at the state and federal levels. Keywords: Policy, Eligibility, Law/Legal Issues Effects of State-Level Funding Systems on Identification Rates of Students with Learning Disabilities The Individuals with Disabilities Education Act (IDEA) mandates states to develop child find systems to ensure students with disabilities are identified and if eligible, served in special education (IDEA, 2004a; IDEA, 2004b). States are attempting to meet student needs while keeping costs low, which has resulted in a myriad of different funding formulas being used across the United States (Ahearn, 2010; Kelly & Whinnery, 2020). The impact of this tension in funding has the potential to influence rates of identification of students for special education. This study reviewed state special education funding formulas, learning disability (LD) identification rates and per pupil spending (PPS) for the year 2016. IDEA The current systems of IDEA funding are rooted in the passage of the Education of the Handicapped Act (EHA, P.L. 91-230, 1970). This law provided a three-year cycle of grants to states to promote the development of programs for students with disabilities (Dragoo, 2019). The reauthorization of the EHA resulted in the Education for All Handicapped Children’s Act (EAHCA) passed in 1975 (Dragoo, 2019). In 1975, the EAHCA directed states to develop structures to fulfill the mandate of providing a free and appropriate public education (FAPE) to all identified, eligible students in special education. A report to Congress noted states were authorized to receive federal funding, after 19

meeting specific eligibility requirements, to be used for “excess costs” related to the provision of special education at the state and local district level (Congressional Research Service, November 14, 1975). Congress provided a mechanism to fund states programs using a per-student allocation multiplied by the average PPS beginning in 1978. The EAHCA sought to increase the funding each year by 5 percent until (theoretically) reaching 40 percent in 1982, thereby creating the “full funding” mandate. At the time the law was implemented, the average federal special education per pupil expenditure (APPE) was 9 percent, which has risen over time to reach approximately 16 percent in 2017 (National Council on Disability, 2018). While there have been many attempts to increase funding rates, the percentage of federal dollars allocated has never approached the 40% full funding amount Congress authorized. This has resulted in states bearing the majority of the costs of special education services with no uniformity in funding methods (Willis et al., 2019). Over forty years later, states have developed a variety of formula mechanisms to provide special education funding for school districts, which can be analyzed around similarities and differences in approaches. Categories of Funding Categories of funding are central to how states fund students in special education. Ahearn (2010) analyzed all 50 states formulas for the provision of special education funding and sorted the formulas into eight categories. The categories included Multiple Student Weights, Census-Based System, Single Student Weights, Resource Based, Percentage Reimbursement, Block Grants, and combinations of the categories that rolled special education funding into overall funding. Twelve states used a system of Multiple Student Weights to allocate funds, making it the most commonly used funding formula in 2010. The remaining systems were evenly distributed with six or seven states using each of the other formulas with the exception of Block Grants, which was being used by only one state. Within her work, Ahearn (2010) noted the variability of each funding system, with states sometimes including additional funding variables such as size and costs (2010). These variables equated to each state funding special education in unique ways. IDEA Prohibitions While the funding mechanism of each state is central to how students are funded for special education, federal legislation also plays a significant role. Ahearn (2010) noted the influence of 1997 and 2004 IDEA federal legislation that worked to ensure state-level mechanisms were, “placement neutral,” thereby seeking to ensure states and LEAs were not financially incentivized to segregate students with disabilities. Language in 20 U.S.C. 1412(a)(5)(b)(ii) specifically bars states from distributing funding based on the setting. While states are precluded from funding based on placement, states have attempted to influence identification rates through other routes. IDEA Compliance In 2018, the Office of Special Education Programs (OSEP) issued a letter to the state of Texas and the education agency commissioner finding the state agency out of compliance for child find, in addition to FAPE and general supervisory and monitoring responsibilities based on their investigation of the state’s performance assessment model (Ryder, 2018). The Texas funding model in question had rewarded districts with identification rates of 8.5 percent or lower with less oversight of their programs, effectively reducing the rate of identified students from 11.6 percent in 2004 to 8.6 percent in 2016. While Texas did not manipulate funding per se, the 20

actions of the state provided evidence of the desire to influence special education identification rates to reduce costs. Current Systems of Funding Approximately a decade after Ahearn’s study, the Education Commission of the States and EdBuild (2019), a self-named “catalyst organization,” again analyzed the current landscape of special education funding formulas (Parker, 2019). Each organization, in a similar fashion to Ahearn (2010), identified 8 types of funding formula methodologies including Single Student Weights, Multiple Student Weights, Resource Based, Census Based, Partial Reimbursement, Block Grants, and Integrated/Non-Separate special education funding. Both groups echoed Ahearn’s (2010) findings and noted the variations that exist across states for funding special education. The majority of states (16-17) were reported as using a system involving Multiple Student Weights. A Multiple Student Weights system is one in which funding is based on the severity of disability (e.g., mild, moderate, severe), the type of disability (e.g., autism, cognitive impairment, etc.), or the type of resources a student receives (e.g., therapies, classroom aide, etc.), which generates specified amounts to the LEAs (Parker, 2019). It should be noted that Kansas was currently in the process of implementing a court-ordered revised funding system and was not included in this analysis (Lecker, 2019). Prior Research on Funding Prominent studies addressing the influence of funding formulas on rates of identification in special education include Greene and Forster (2002) and, in response, Mahitivanichcha and Parrish (2005a). Greene and Forster (2002) completed an analysis of the rates of identification for special education students across a decade beginning in 1991and ending in 2001. Comparing two types of state funding systems, Bounty Sum and Lump Sum, the researchers concluded the increase in special education rates was attributable not to better systems for finding students with actual disabilities, but rather to the Bounty Sum system promoting identification of more students (Greene & Forster, 2002). Bounty Sum and Lump Sum funding systems correlated to a Census-Based System and Multiple Student Weights systems identified by Ahearn (2010). The Greene and Forster (2002) study provided recommendations that states should adopt Lump Sum (i.e., Multiple Student Weights) funding formulas to stem the increase of students in special education, in addition to other recommendations regarding vouchers and more federal oversight of special education placements. Fiscal Incentives for Identification The question of whether or not states are incentivized to identify more or less students for special education is critical in the discussion of varying state funding mechanisms for special education. Mahitivanichcha and Parrish (2005a) discussed the question of fiscal incentives and special education funding mechanisms in detail. The authors acknowledged the research evidence supporting the influence of various funding formulas on identification, but also countered with a number of modifiers to those systems such as “historical context, impact of advocacy groups or organizational structure, professional judgement, program constraints, and government regulations” (Mahitivanichcha & Parrish, 2005a, p. 40). The researchers duplicated the Greene and Forster (2002) study but adjusted the variable of high stakes testing, deemed to be statistically insignificant in the original study, with rates of poverty. Poverty was found to be a 21

statistically significant factor in special education rates of identification. They also considered the influence of including California in the Greene and Forster (2002) study. California, a state representing one-eighth of the nation's population, moved from a non-census to Census-Based System in 1997. Mahitivanchcha and Parrish (2005a) removed California from the comparisons of the two systems and the effect showed a reversal of the trends cited by Greene and Forster (2002) for increases in enrollments. Ultimately, the 2005 study found a much smaller but consistent agreement with Greene and Forster (2002) that special education rates of identification were lower (0.06%) in Census-Based Systems. Mahitivanichcha and Parrish (2005b) called the concern about identification of students solely for the purpose of generating funds “unlikely” (p. 5). They argued while efforts to inflate student special education numbers are not the intended action of team members, they are influenced by what their state systems provide and the array of options available for meeting students’ needs. In considering funding formulas as a singular factor in influencing rates of identification of students, Mahitivanichcha and Parrish (2005b) identified a moderate connection being established between state program decision-making and funding methodology. Trends in Rates of Identification With the results of these studies now over a decade old (Greene & Forster, 2002; Mahitivanichcha & Parrish, 2005a; Mahitivanichcha & Parrish, 2005b) a review of the current system is warranted. Rates of enrollment in special education reveal a decline between 2004 and 2012, with the numbers falling to 12.9 percent in 2012 (National Center for Educational Statistics, 2019b). Since that time, overall enrollment has risen slightly over time, and the most recent publicly reported data from 2017-18 for students, ages 6-21 who receive special education, is 13.7 percent (National Center for Education Statistics, 2019). The National Center for Education Statistics (NCES) reported the 2018-19 child count showed 33 percent of the total number of students with disabilities were students with specific learning disabilities (2019a). LD continues to represent the largest group among the 16 federal special education categories despite reductions in identification rates during this time period. The National Center for Learning Disabilities (NCLD) noted 45 states reported a drop in the percentage of students with LD in special education from 2004 through 2011 (Cortiella & Horowitz, 2014). Purpose of the Study This study reviewed state special education funding formulas, learning disability (LD) identification rates and per pupil spending (PPS) for the year 2016. The purpose of this study was to determine if state special education funding formulas were predictive of LD diagnosis and PPS, as well as whether per pupil spending (PPS) correlated with the rate of LD identification. Method To conduct this study, states were grouped according to the reimbursement model of special education funding used. The types of funding formulas reflect the groupings used by the Education Commission of the States and EdBuild. State funding formulas included: Single Student Weight, Census-Based System, Resource Allocation Model, Reimbursement System, Block Grant, High-Cost Students System, and Multiple Models. States using Multiple 22

Reimbursement Models were grouped together. They included Alaska, Arizona, Florida, Maine, Massachusetts, New Hampshire, New Jersey, Rhode Island, Vermont, West Virginia, Illinois, Montana, South Dakota, Minnesota, and Wisconsin. Rates of identification of students with LD for 2016 and PPS were collected through online data sources for all 50 states. After being grouped according to the funding formula used by the state, the average LD identification rate and PPS rate were calculated for each respective special education funding formula. Data Sources Learning Disability Rate To determine LD identification rates for each state, two sources of data were collected. The numerator was calculated using the United States Department of Education Office of Special Education Program 2016 child count data for ages 3-21 for students identified under the category of learning disability. The denominator was calculated using data from the National Center for Education Statistics (NCES) enrollment in public elementary and secondary schools. The NCES table was updated in March 2019 to include the most recent data available from fall 2016. To calculate LD identification rate, the numerator was divided into the denominator to get a percentage value. Per Pupil Spending (PPS) Data for 2016 PPS were obtained from the Governing website, which summarized total PPS by state. PPS data included non-personnel expenses and was a combination of instructional and support services spending. Special Education Funding Formulas Seven special education funding formulas were used for this study, and an eighth reimbursement category was created to represent states that use multiple funding models called, “Multiple Reimbursement Models.” States that used Multiple Reimbursement Models were not included in each individual funding formula to avoid skewing the data. Funding formulas for each state are presented in Table 1 and each funding formula is described below. Table 1 Reimbursement Model by State Reimbursement Model Multiple Student Weights

States Using Model CO, GA, IN, IA, KY, NM, OH, OK, PA, SC, TX

Single Student Weight

LA, MD, MO, NV, NC, ND, OR, WA

Census-Based System

AL, CA, ID

Resource Allocation Model

DE, HI, MS, TN, VA

Reimbursement System

KS, MI, NE, WY

Block Grant

UT

High-Cost Students

AR, CT 23

Multiple Reimbursement Models*

AK, AZ, FL, ME, MA, NH, NJ, RI, VT, WV, IL, MT, SD, MN, WI

*Multiple Reimbursement Models refers to states using more than one model for reimbursement.

Multiple Student Weights The Multiple Student Weights (MSW) system is a formula that assigns funding to a student based on factors related to the severity and type of disability. In this formula, a school district would receive funding for the severity of the disability as well as the type of disability (e.g., LD). Single Student Weight The Single Student Weight system allows school districts to receive funding on a per student basis. Regardless of the severity or type of disability, a district receives funding based on the number of students identified with disabilities. Census-Based The Census-Based system operates under the assumption that each school district in a state has roughly the same percentage of students who require special education services. Funding is provided to school districts based on the size of the district, with the assumption of percentage of disabilities used as the primary indicator of necessary funding. Resource Allocation Model The Resource Allocation Model provides resources, not funding dollars, to school districts based on the number of identified students requiring special education services. States using the Resource Allocation Model provide teachers, support staff, and additional services staff (e.g., Speech-Language Pathologist) to provide services for the students. Reimbursement System The Reimbursement System model allows school districts to submit special education expenses to the state, and the state determines if they will reimburse all or a portion of the expenses that have been submitted. Block Grant The Block Grant model provides funding from the state to be used for special education services. This model may be calculated based on spending in the previous year. High-Cost Students The High-Cost Students system allows states to provide funding based on the number of highcost students in the district. This system is often coupled with another funding model to off-set the costs of special education services up to a certain threshold. Multiple Reimbursement Models The Multiple Reimbursement Models category accounts for states that use multiple funding models based on the funding formulas used above. For example, the state of South Dakota uses both the Census-Based system and Multiple Student Weights system to fund special education services, in addition to having a system for funding high cost students or programs. 24

Procedure For this study, data were collected from all 50 states. Several data points were not available for the District of Columbia, Bureau of Indian Affairs, Northern Mariana, American Samoa, Guam, Puerto Rico, and the United States Virgin Islands, resulting in their exclusion from the study. Additionally, Wisconsin was included in the study despite child count data being unavailable. Data for each state were transferred to excel tables based on their funding formula or use of Multiple Reimbursement Models. LD rates and PPS were calculated for each state, and mean scores were calculated for each funding formula. Data Agreement Data agreement was reached by independent review of the data collected for this study. The data collection was aggregated from source websites and placed into Excel spreadsheets by the first researcher. The second researcher did a check of the data placed into the spreadsheets for accuracy. Overall, no errors were identified by the 2nd reviewer. A second review of data was conducted by the first researcher and confirmed the presence of no errors or omissions. Data Analyses Mean LD Identification Rate and Mean PPS States were divided into one of eight categories according to the reimbursement model they used to fund special education. For each of the eight categories, mean LD identification rates and mean PPS were calculated for each special education funding model. Comparison of Special Education Reimbursement Model versus LD Identification Rate and PPS A one-way ANOVA was performed to determine the effect of reimbursement model on LD identification rate, and the effect of special education reimbursement model on PPS. Data were analyzed using SPSS (Statistical Package for the Social Sciences). State-Level Correlation of LD Rate and PPS A Pearson Product Moment Correlation was used to determine if a relationship existed between LD identification rate and PPS. For this analysis, state-level data were used to determine if a state’s LD identification rate correlated with a state’s PPS. Data were analyzed using SPSS (Statistical Package for the Social Sciences). Grouped State Comparison of Mean PPS and Mean State LD Identification Rate States were divided into two groups for an additional comparison of states who rank in the top half of all states in PPS versus states who rank in the bottom half of all states in spending per pupil. States that were ranked 1-25 in spending were placed into group 1, while states ranked 2650 in spending were placed into group 2. The two groups were compared on aggregate mean rates of LD identification.

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Results This study reviewed state special education funding formulas, learning disability (LD) identification rates and per pupil spending (PPS) for the year 2016. The purpose of this study was to determine if state special education funding formulas were predictive of LD diagnosis and PPS, as well as whether per pupil spending (PPS) correlated with the rate of LD identification. Mean LD Identification Rate and Mean PPS Mean LD identification rate and mean PPS are reported in Table 2. The results indicated that states using the Multiple Student Weights formula had the highest LD identification rate, while states using the Block Grant and Multiple Reimbursement Models formulas evidenced the second and third highest LD identification rates, respectively. Table 2 LD Rate, Per Pupil Spending, and Average National Ranks by Reimbursement Model Reimbursement Model LD Identification Average Per Pupil Rate Spending (PPS) Multiple Student Weights 5.21% $10,411 Block Grant

5.14%

$6,953

Multiple Reimbursement Models

5.03%

$13,326

Reimbursement System

4.58%

$12,592

Resource Allocation Model

4.55%

$11,481

Single Student Weight

4.37%

$12,676

High-Cost Students

4.34%

$14,402

Census-Based System

3.82%

$9,296

* Note: Multiple Reimbursement Models data does not include LD Identification Rate for Wisconsin due to the data being unavailable

Comparison of Reimbursement Model versus LD Identification Rate and PPS Results of the one-way ANOVA are reported in Table 3. Results indicated that special education reimbursement model did not predict either LD identification rate or PPS. Table 3 The relationship between special education reimbursement model, LD identification rate, and PPS. Variable Sum of Squares df Mean Square F p LD Identification Rate

Between Groups

6.974

7

.996

Within Groups

51.079

41

1.246

26

.800

.592

Total

58.053

48

Between Groups

113478167.740

7

16211166.820

Within Groups

441228892.760

42

10505449.828

Total

554707060.500

49

PPS

1.543

.179

*Sig. at p < .05

State-Level Correlation of LD Rate and PPS The Pearson-Product Moment Correlation between LD rate and PPS is reported in Table 4. The relationship between the rate of LD and PPS was examined at the state level. Results indicated the relationship between LD rate and PPS was not statistically significant (r = .278, p < .053), indicating that the rate of LD was not correlated with the amount of PPS for the state. Table 4 Pearson-Product Moment Correlation of LD Rate and PPS. Learning Disability Learning Disability Pearson’s r

Per Pupil Spending .278

p-value Per Pupil Spending

.053

Pearson’s r p-value

**p < .05

Grouped State Comparison of Mean PPS and Mean State LD Identification Rate The results of states ranked 1-25 in spending per pupil (Group 1) versus states ranked 26-50 in spending per pupil (Group 2) are summarized in Table 5. For Group 1, the LD identification rate was 4.87%. Group 2, in contrast, evidenced an LD identification rate of 4.52%, which is a 7.5% difference between the two groups. The results indicate that the states ranked 1-25 in PPS identified students with LD at a higher rate than states ranked 26-50 in PPS. Table 5 LD Average Identification Rates for States Ranked 1-25 v. 26-51 in Per Pupil Spending (PPS) Per Pupil Spending (PPS) Rank Learning Disability (LD) Average Identification Rate States Ranked 1-25 in PPS 4.87% States Ranked 26-50 in PPS

4.52%

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Discussion The purpose of this study was to determine if state special education funding formulas were predictive of LD diagnosis and PPS, as well as whether per pupil spending (PPS) correlated to the rate of LD identification. At the time of this study, the most current data reflected many states headed the call by Mahitivanchcha and Parrish (2005a) to adopt multiple student weight models, with 11 states using the Multiple Student Weight model compared to only 3 states using a Census-based model. In considering the effects of state funding formulas on the identification of students with LD, it was found that states using Multiple Reimbursement Models ranked near the top in LD identification rate (third) and PPS (second). The only funding formulas with a higher average percentage rate of LD identification were Multiple Student Weights and the Block Grant system (i.e., Utah), while the only funding formula with a higher average PPS was the High-Cost Students System. In contrast, states using a Census-Based System represented the lowest average LD identification rate and had nearly the lowest average national ranking in PPS. If Block Grant systems, which included only one state (i.e., Utah) were removed from the cost analysis, the Census-Based System averaged the lowest PPS and the lowest rate of identification of students with LD. In 2002, Greene and Forster strongly discouraged bounty (i.e., Census-Based System) systems to counteract unchecked growth in rates of identification. Their findings contrast with current results that reflect Census-Based Systems as being less costly and providing the lowest rates for identification of students with LD. The three states using a High-Cost Student system of funding (i.e., Arkansas, Connecticut, and West Virginia) presented widely divergent PPS, which brings the usefulness of this funding system into question. Connecticut, ranked 3rd in student spending, skews the ranking as compared to the other two states, which had much lower spending amounts. Large State Trends Mahitivanchcha and Parrish (2005a) noted the impact of including states with high student populations in analysis of funding systems related to special education identification rates. In looking at the data from the states with the largest numbers of students identified with LD (i.e., California, New York, Texas, Florida, Illinois, and Pennsylvania) a number of findings emerged. California, using a Census-Based System, was ranked 23 rd in LD identification and 23rd in PPS. New York, using a Single Student Weight system, ranked 1st in PPS while also being in the upper tier (i.e., 5th) of states for LD identification rates. Texas, using a Multiple Student Weights system, reported an LD identification rate of only 2.93%, ranking 48 th across the states, with a PPS ranking of 42nd. Texas remains an outlier among the large states due to its recent federal findings of suppression of child identification and noncompliance with child find (Ryder, 2018). Pennsylvania, also using a Multiple Student Weights system, was on the opposite side of LD identification rates, ranking 3rd among all states in LD identification rate, while ranking 11th in PPS. Florida and Illinois, both using Multiple Reimbursement Models, were nearly identical in their rates of LD identification. Florida identified students with LD at a rate of 5.06%, compared to 5.05% in Illinois, while the two states were ranked 44 th and 14th in PPS, respectively. The wide ranges in ranking of PPS supports the caution illustrated by Mahitivanchcha and Parrish 28

(2005a) in considering adoption of special education funding formulas, with population and poverty skewing results. National Trends Results of states spending more per pupil (i.e., states ranked 1-25 in PPS) were compared to states who spend less per pupil (PPS) to see if differences existed in LD identification rate. These results, presented in Table 4, did show a difference of 4.87% to 4.52%, indicating states with a higher PPS identify students with LD at higher rates. While this analysis did provide a different view of LD identification rates, it did not take into account various funding mechanisms or specific state characteristics (e.g., rural states). Policy Implications Funding systems for special education are often the target of state legislatures and governors looking to reduce costs. In 2016 alone, tracking done by the Education Commission of the States (ECS) identified 13 states enacting legislation to address changes in funding for special education. By 2021, 185 bills were proposed surrounding education funding, with 32 adopted that directly addressed special populations. Education finance in the area of special education emerges consistently across legislative and gubernatorial priorities (Kelly & Whinnery, 2020). The gap between the percentage of students identified with learning disabilities in states with funding based on multiple student measures compared to those using census-based systems supports higher identification rates of learning disabilities can be expected when funding is aligned to weights based on the severity and type of disability. Learning disabilities are generally positioned as less costly in terms of services required for specialized instruction (Willis et al., 2019), meaning states adopting multiple weights likely are not seeking to limit this category. The question that exists within this discrepancy is not about how the money is distributed, but the adequacy and authenticity of child find systems from state to state. The variability in rates of identification means access to FAPE is influenced by the state of residence and dedicated education spending. As state and local school administrators and legislators work to make costs stretch to meet budgetary demands, the issue of meeting the intent of IDEA is at the heart of every funding model. The goal of any funding system should be to ensure states may adequately identify and meet the needs of all students in special education. Ensuring the adequacy of state child find systems is under the purview of OSEP federal monitoring systems. The current system of federal oversight, Results Driven Accountability (RDA), is described as focused on student results versus compliance (Delisle, 2014). This accountability process employed by OSEP does not consider child find data in any variation beyond checking timelines for compliance. As a result, states act without ongoing federal oversight for the effectiveness of their child find procedures. Texas is the most recent visible example of enacting a funding system that suppressed identification, and the state stayed under the radar for years until OSEP acted (Ryder, 2018). While ensuring the timeliness of completing evaluations under child find is important, the RDA system should be expanded to consider the results of those evaluations in terms of the number and percentage of students identified for

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services under each of the 13 federal categories of eligibility, including students with learning disabilities. Limitations Three primary limitations were identified in this study. First, a lack of state funding data specific to special education was available for analysis. After conducting a comprehensive search across states, regions and at the national level, data on spending specific to students in special education was not identified. Additionally, PPS was identified for each state but did not provide the ability to further discriminate for funding specific to special education. Second, data for LD identification was not available for the state of Wisconsin. Wisconsin funds special education using a Multiple Reimbursement Model. PPS was available for Wisconsin and is included in the analysis. Third, state characteristics with respect to population differences and travel within rural school districts was not considered as part of this analysis. Future Research Other areas to be explored include determining if the range of funding models used have an impact on rates of identification for other disability areas, similar in severity such as other health impairment (OHI) or contrasting with more significant needs like autism spectrum disorders (ASD). The influence of poverty related to funding and identification rates is another area that warrants further research. Considering the tax structures and socioeconomic status for individual states may provide further understanding of the differences both in funding and rates of identification for students with LD. Another trend noted in the data for this study was the influence of highly populated states as compared to lower, less densely populated states, as well as political affiliations such as red or blue states that dominate the states (Elving, 2014). Conducting an analysis of rates of identification specific to state populations may also provide more understanding of trends in identification. Finally, state accountability to the federal government through an analysis of state performance results may also render new ideas about the approaches states are taking in their funding structures. Questions exist regarding whether funding systems have an effect on determinations made regarding states that meet requirements under IDEA or need assistance (U.S. Department of Education, OSEP, 2020). Conclusion Funding remains stagnant at the federal level, meaning states will continue to consider and reconsider their systems, both for funding and identification, to meet the needs of local school districts, students, and families. While various funding formulas were not predictive of identification rates or PPS, it was found that states with higher PPS identified students with learning disabilities at a higher rate. This translates to children who will potentially go unidentified in states that commit less funding to education per student. Although IDEA (2004a) requires all states to implement child find for learning disabilities, federal oversight of the effectiveness of these systems is absent. The influence of compliance with federal regulations needs further study to determine what variables across states further influence the identification rates of students with LD.

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References Ahearn, E. (2010). Special education: State funding formulas. Project Forum, National Association of State Directors of Special Education. Alexandria, VA. Cortiella, C. & Horowitz, S.H. (2014). The state of learning disabilities: Facts, trends and emerging issues. New York: National Center for Learning Disabilities. Congressional Research Service. (1975). Congressional report: Education of All Handicapped Children Act. Library of Congress Delisle, D.S. (2014). Dear colleague letter to Chief State School Officers on Results-driven Accountability (RDA) [letter]. United States Department of Education https://www2.ed.gov/about/offices/list/osers/osep/rda/050914rda-lette-to-chiefs-final.pdf Dragoo, K.E. (2019). Congressional report: Education of All Handicapped Children Act (version 4). Congressional Research Service. Retrieved from https://crsreports.congress.gov/product/pdf/R/R44624 EdBuild. (2019, December 16). FundED: National policy maps. Retrieved from: http://funded.edbuild.org/national#special-ed Education Commission of the States. (2021, July 16). State Education Policy Watchlist. Retrieved from https://www.ecs.org/state-education-policy-watch-list/ Elving, R. (2014, November 13). The color of politics: How did red and blue states come to be? National Public Radio. https://www.npr.org/2014/11/13/363762677/the-color-of-politicshow-did-red-and-blue-states-come-to-be GOVERNING. (2020, June 2). Education spending per student by state. Retrieved from: https://www.governing.com/gov-da ta/education-data/state-education-spending-perpupil-data.html Greene, J.P. & Forster, G. (2002). Effects of funding on special education enrollment. Civic report No. 32. Center for Civic Innovation, Manhattan Institute, New York, NY. Individuals with Disabilities Education Act. 20 U.S.C. §§ 1400-1415 (2004a). Individuals with Disabilities Education Act. (2004b). 34 C.F.R § 300.111 Child find. [IDEA regulations] https://sites.ed.gov/idea/regs/b/b/300.111 Kelly, B. & Whinnery, E. (2020). Governors’ top education priorities in 2020 State of the State address. [Special Report] Education Commission of the States and National Governors Association. Lecker, M. (2019, June 24) Kansas high court to state: School funding formula adequate, now fund it. Education Law Center. Retrieved from: awcenter.org/news/archives/otherstates/kansas-high-court-to-state-school-funding-formula-adequate,-now-fundit.htmlhttps://edl Mahitivanichcha, K., & Parrish, T. (2005a). Do non-census funding systems encourage special education identification? Reconsidering Greene and Forster. Journal of Special Education Leadership, 18(1), 38-46. Mahitivanichcha, K., & Parrish, T. (2005b). The implications of fiscal incentives on identification rates and placement in special education: Formulas for influencing best practices. Journal Education Finance, 31(1), 1-22. National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education (2019). Students with disabilities. Retrieved from: https://nces.ed.gov/fastfacts/display.asp?id=64#:~:text=After%202004%E2%80%9305% 2C%20the%20percentage,4.7%20percent%20in%202018%E2%80%9319. 31

National Council on Disability. (2018). Broken Promises: The underfunding of IDEA. National Council on Disability, Washington D.C. Parker, E. (2019, December 16). State education policy tracking. Education Commission of the States. Retrieved from: https://www.ecs.org/state-education-policy-tracking/ Ryder, R.E. (2018). Letter to Morath. U.S. Department of Education, Office of Special Education and Rehabilitation Services, Office of Special Education Programs. Retrieved from https://www2.ed.gov/fund/data/report/idea/partbdmsrpts/dms-tx-b-2017-letter.pdf U.S. Department of Education, Office of Special Education Programs. (2020). Part B State Performance Plans (SPP) and Annual Performance Report (APR) letters [Report]. Retrieved from https://www2.ed.gov/fund/data/report/idea/partbspap/allyears.html Willis, J., Doutre, S.M., & Berg-Jacobson, A. (2019). Study of the individualized education program (IEP) process and the adequate funding level for students with disabilities in Maryland. San Francisco, CA: WestEd. About the Authors Brad M. Uhing, Ph.D., is an Assistant Professor in the School of Education at Augustana University, where he teaches undergraduate and graduate courses in assessment, research, psychology, and inclusion. His current research interests include assessment, early intervention, and education policy. Michelle Powers, Ed.D., is an Assistant Professor in the School of Education at Augustana University, where she teachers undergraduate and graduate courses in foundations and methods for cognitive and learning disabilities, as well as educational policy and special education law. Her research interests include educational policy focused on special education funding and LRE issues. Other work experiences include policy and legislative work as the state director of special education for the state of South Dakota, as well as serving at the local school district level as a director of special education. She holds current leadership positions in the SD CEC as president and has previously served on the NASDSE board of directors and as president of SD CASE.

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Stress-Management Interventions for Special Education Teachers: A Systematic Literature Review Haya Abdellatif, M.S., M.Ed. Rachel Robertson, Ph.D. University of Pittsburgh Abstract High stress levels experienced by special education teachers drive many to leave their jobs or leave the field altogether. The purpose of this systematic literature review was to examine the effectiveness of stress-management intervention programs that were implemented to help special education teachers cope with daily job-related stressors. Three empirical studies met inclusion criteria. In all three studies, stress-management interventions were found to improve different stress-related measures with varying degrees of effectiveness. Research and practical implications are discussed regarding how special education teachers’ experiences in the field could be examined more deeply and how schools could provide more responsive support. Stress-Management Interventions for Special Education Teachers: A Systematic Literature Review The special education field has been confronting high levels of teacher shortages for more than a decade now (AAEE, 2017; Billingsley & Bettini, 2019; Boe et al., 2006; Grant, 2017). This shortage has been found to be largely due to teacher attrition which takes place when teachers either leave their special education teaching jobs in favor of general education teaching positions or when they leave teaching altogether (Hagaman & Casey, 2017). Even though studies identified several factors to be associated with the attrition of special education teachers, most of them had one thing in common; they were closely related to the experience of heightened stress levels (Miller et al., 1999). Experiencing high levels of stress has, thus, been identified as a major contributor to teacher attrition in the special education field (Billingsley, 1993; Hagaman & Casey, 2017; Miller et al., 1999). Work-Related Stress and Burnout In 1956, Hans Seyle, a Hungarian-Canadian endocrinologist, coined the term “stress” to describe the process by which an individual’s biological equilibrium, “homeostasis”, becomes threatened (Schneiderman et al., 2005). Even though experiencing stress is recognized as an adaptive evolutionary response to threatening circumstances, it is the prolonged experience of stress that can be potentially dangerous to an individual (Schneiderman et al., 2005). Employees in peopleoriented professions, such as teachers, who are expected to dedicate their services to the wellbeing of others, are especially vulnerable to experiencing prolonged periods of stress which, in turn, may result in job burnout (Maslach & Leiter, 2016). Maslach (2003) describes job burnout as a “psychological syndrome that involves a prolonged response to stressors in the workplace” and constitutes of three primary dimensions: (a) feeling overwhelming exhaustion, (b) experiencing cynicism which might manifest in the form of emotional and cognitive detachment from one’s job, and (c) having a sense of inefficacy (pp. 189-190). Work-related factors that lead 33

to job burnout include difficult job demands, insufficient resources, and perceived conflict between employees or between one’s job expectations and job demands (Dicke et al. 2018; Maslach, 2003). Unique Contributors to Stress in the Special Education Field As a people-centered occupation, teaching has been described as a high stress profession (Perry et al., 2015). Teachers in the special education field, however, have been found to experience higher stress levels than general education teachers (Kiel et al., 2016; Miller et al., 1999). This tendency is due to factors that are uniquely found in the special education field. Factors within the special education field that have been found to contribute to heightened stress levels, include: (a) large amounts of paperwork, (b) a high rate of meetings compared to other staff members, (c) attending to a wide range of student disabilities, (d) managing emotionally and behaviorally challenging students, (e) large caseloads, and (f) extensive federal and state mandates (Cancio et al., 2018; Grant, 2017; Kiel et al., 2016; Leko & Smith, 2010; Miller et al., 1999; Westling et al., 2006). Consequences of Teachers’ High Stress Levels Special education teachers have been found to experience a range of detrimental effects while experiencing high levels of stress. Teachers who consistently suffer from high stress and burnout are more likely to experience illnesses such as chronic fatigue, the flu, muscular pains, and depression (Brunsting et al., 2014). In addition, teachers who experience high stress levels were found to implement evidence-based practices with lower levels fidelity (Larson et al., 2018). Since special education teachers are expected to provide their students with various academic, emotional, and behavioral evidence-based practices, experiencing high stress could compromise the quality with which they might deliver them. Moreover, students whose teachers experienced high stress were more likely to struggle academically and behaviorally (Brunsting et al., 2014; Klusmann et al., 2016) as well as have lower quality interactions with their teachers (Sandilos et al., 2018). Addressing teachers’ stress levels could, therefore, lead to far reaching effects that would benefit not only teachers’ but students’ well-being as well. Coping Mechanisms In attempting to cope with their stressors, teachers in special education were found to engage in several different practices. In one study, listening to music and seeking support from family and friends were the most common coping strategies reported while dancing was found to be the only strategy that was associated with lowered stress levels (Cancio et al., 2018). Special Education teachers also reported that exercising and talking with colleagues helped alleviate their stress (Perry et al., 2015). Special Education pre-service teachers were found to engage in similar coping strategies as those mentioned above; several also shared that time management and taking time to practice enjoyable activities such as reading, reflecting, and yoga were helpful ways they were able to reduce their stress levels (Paquette & Rieg, 2016). Even though many special education teachers seemed to engage in different coping strategies on their own account, researchers have emphasized the need for school districts to take more proactive steps in supporting those teachers in their coping efforts. More specifically, researchers have urged schools to develop and implement comprehensive stress-management programs for special education teachers due to the unique stressors they confront in school (Ansley et al., 2016).

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Purpose of This Review Although previous literature reviews were carried out on studies that examined special education teachers’ stress and burnout (Brunsting et al., 2014; Wisniewski & Gargiulo, 1997), none have focused specifically on studies that examined the effectiveness of stress-management interventions for special education teachers. The purpose of this paper, therefore, is to review intervention efforts that have been carried out to support special education teachers in coping more effectively with work-related stress and burnout. Specifically, this study sought to answer the following research questions: Research Question 1: What interventions have been implemented to help special education teachers cope with stress in their schools? Research Question 2: What effect did these interventions have on special education teachers’ experiences of stress and stress-related measures of well-being? Method A systematic search was conducted in order to identify studies that have reported the effectiveness of interventions on the stress levels of special education teachers. An electronic search was carried out through ERIC, PsycInfo, PsycArticles, and The Psychology Database. The search included all truncations and derivations of the following terms: (“special educator” or “special education teacher” or "special education instructor") AND ("teacher stress" or "teacher emotion" or "teacher psychology” or "teacher health" or "teacher well-being" or "teacher wellbeing" or "teacher coping" or "teacher support" or “teacher burnout") AND ("intervention" or "program" or "training"). Results included articles published between 1990 and 2020. This search yielded a total of 574 articles. An article was accepted for this review if it met all the following inclusion criteria: 1. Published in a peer-reviewed journal. 2. An empirical or evaluative study based on data collected before and after an intervention or program was implemented. Theoretical papers on strategies to help teachers cope with stress (e.g. Emery & Vandenberg, 2010; Leko & Smith, 2010) or descriptive studies such as papers describing special education teachers’ reflections or thoughts on how they coped with stress (e.g. Cancio et al., 2018; Perry et al., 2015) were excluded. 3. Included special education teachers in their sample of participants. 4. Reported a stress-related construct as an outcome measure of well-being. Articles that mentioned addressing stress but did not provide outcome results on stress or stress-related measures of well-being were excluded (e.g., Westling et al., 2006). 5. Took place in the United States. Field-specific circumstances and work conditions as experienced by special education teachers through the public-school systems, federal 35

mandates, and student challenges are expected to be different across countries. Since this study is based on special education teachers in the United States, only programs or interventions that were carried out in the United States were reviewed. The first author reviewed all the titles and read the abstracts of titles that mentioned special education teachers and any aspect of well-being. Titles of studies that referred to interventions targeting students, parents, administrators, paraprofessionals, or general education teachers or interventions targeting special education teachers that were not relevant to supporting their wellbeing, such as bullying education, were not examined further. This process helped the author retain three articles. An ancestral search was then carried out by reviewing the reference lists of all three articles, but no additional articles were identified. Finally, a hand search was carried out through journals in which the three articles were found (i.e., Exceptional Children, Developmental Psychology, and Topics in Early Childhood Special Education) by using the search terms “special education” and the different possible truncations of “teacher” within each journal’s website. The titles and abstracts of titles that mentioned the well-being of special education teachers were reviewed, but this search did not yield any additional articles. Data Analysis The first author read the three studies closely several times to extract data that were relevant to the purpose of this review. Data extracted included teachers’ demographics, intervention settings, research designs, types and descriptions of the interventions used, stress-related outcome measures, quality of the studies, and reported findings. The first and second authors held numerous meetings to discuss the relevance of the data extracted and further refine the scope of the data to report on in the current review. Results Table 1 illustrates brief descriptions of the three reviewed articles. Participants All three studies had a total number of 97 staff members who worked with students with disabilities. One study provided a clear breakdown of the specializations of the participants, indicating that 34 of their participants in total (between their control and treatment groups) were special educators (Cooley & Yovanoff, 1996). The remaining studies either grouped professionals who worked with students with disabilities into a single category which included special education teachers, assistant teachers, and administrative staff (Biglan et al., 2013) or mentioned that they were educators of students with disabilities without explicitly specifying that they were special education teachers (Benn et al., 2012). Teachers’ gender distribution was provided by one study where 32 teachers were described as female and 3 as male (Benn et al., 2012). One study was limited to educators serving students within a specific age range and grade-level due to its taking place in a preschool (Biglan et al., 2013). Participants in the remaining two studies were described as working with students of a variety of ages and grades, particularly with middle and high school students in Benn et al.’s (2012) study; and elementary, middle, and high school in Cooley and Yovanoff’s (1996) study. Specific disability types that teachers were described as working with were developmental disabilities (Benn et al., 2012; Cooley & Yovanoff, 1996); and autism, ADHD, learning disabilities, and cognitive or health 36

impairments (Benn et al. 2012). Biglan et al. (2013) did not provide a disaggregated breakdown of disability types, stating instead that students had a variety of disabilities. One of the studies provided information regarding the number of years their special education teachers had been at their jobs, sharing that 66% of the participants had worked for five years or less (Cooley & Yovanoff, 1996). None of the studies provided a breakdown of the number of years the special education teachers in their studies had been in the special education profession in general, nor did they include their areas of certification. Setting Information pertaining to the nature of the settings where the interventions were implemented was limited across all three studies. One of the studies described their setting as a preschool for students with developmental disabilities (Biglan et al., 2013). The remaining studies did not specify where their interventions took place- whether they were held at schools, at community centers, or district offices, for instance. In addition, two studies briefly mentioned the geographical location where their studies took place; Biglan et al. (2013) shared that their study was implemented in a northwest county while Benn et al. (2012) stated that their study took place in a small midwestern city. None of the studies provided details on such descriptors as level of urbanicity, poverty, and diversity of the school or program where the interventions took place.

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Table 1 Summary of Studies Study Participants Benn et al. 35 SE (2012) 32 F 3M

Biglan et al. (2013)

28 SE 2 Adm. 12 FC

Approach Stress Management and Relaxation Techniques (SMART)

Acceptance and Commitment Therapy (ACT)

Procedure Didactic and group discussion sessions, mindfulness practices, Homework assignments, and different topics and sets of practices covered each session.

Duration Nine sessions-2.5 hours each and two full days.

Different workshops presented in each session where teachers practice skills such as mindfulness, reflecting on values, and accepting negative thoughts.

Two 3.5-hour workshops

Study Design Randomized waitlist control design

Twice a week over a five-week period.

Randomized waitlist control design

Two weeks apart for immediate group Three weeks apart for delayed group.

Cooley & Yovanoff (1996)

34 SE 17 RS 16 Oth.

Two interventions: Stress management

All participants experienced both interventions.

Peer collaboration

Stress management intervention: Interactive presentations, small/large group discussions, applications during sessions, practice between sessions 38

Stress management intervention: Five 2- hour workshops Peer collaboration program:

Randomized modified crossover control design

Study

Participants Approach

Procedure Topics covered: Situational coping skills, physiological coping skills, cognitive coping skills. Peer Collaboration program: Pairs of teachers trained to carry out specific steps and fill out a form with responses to each: Clarify problem

Duration One 3-hr training session and 2-hr sessions each week for four weeks.

Study Design

Summarizing problem Intervention and prediction Evaluation of solution Note. SE = Special Education, GE = General Education, Adm = Administration, Oth.= Other, RS = Related Service, FC = Family Consultants, F = Female, M = Male.

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Research Designs Each of the three studies followed an experimental study design with subtle differences in the ways their procedures were implemented. All three studies constituted random assignments of participants into either treatment groups or waitlist control groups where participants were provided with the interventions once the treatment groups completed all the intervention sessions. The three studies collected data before and after the interventions for control and treatment groups. In addition, follow-up data was collected by Benn et al. (2012) two months after the intervention, by Biglan et al. (2013) three months and four and a half months after the intervention, and by Cooley & Yovanoff, (1996) six months and one year after the intervention was concluded. Two of the three studies collected both quantitative and qualitative data in the form of surveys and open-ended questions (Biglan et al., 2013; Cooley & Yovanoff, 1996) while the remaining study collected only quantitative measures in the form of survey responses (Benn et al. 2012). Dependent Variables Stress and Stress-Related Measures of Well-being The three studies used a variety of surveys to capture their participants’ experiences of stress. Biglan et al. (2013) and Cooley and Yovanoff (1997) used the Maslach Burnout Inventory (MBI) to measure teachers’ burnout. In addition to using the MBI, Biglan et al. (2013) used the Teacher Characteristics subscale of the Index of Teaching Stress (ITS) to explore how teachers’ interactions, thoughts, and feelings influenced their experiences of stress in pertinence to their students. Benn et al. (2012) used the Perceived Stress Scale (PSS) which captured the extent to which participants experienced stress levels the month prior to the survey. The authors used additional instruments to measure stress-related indicators of well-being such as depression (Benn et al., 2012); teaching efficacy (Benn et al. 2012; Biglan et al. 2013); negative affect, anxiety, self-compassion and forgiveness (Benn et al. 2012); and job satisfaction (Biglan et al., 2013; Cooley & Yovanoff, 1996). Independent Variables Intervention Types One of the main ways the three studies provided teachers with support was to sharpen their cognitive and emotional coping skills. In doing so, teachers were trained to regulate their negative thoughts and emotions in order to alleviate their stress and prevent potential burnout. Benn et al. (2012) implemented a mindfulness approach called the Stress Management and Relaxation Techniques (SMART). In their paper, they stated that mindfulness techniques enable practitioners to exercise the deep levels of focused attention, thought flexibility, and emotional regulation necessary for working effectively with students with disabilities and challenging needs (Benn et al. 2012). Even though mindfulness techniques were also incorporated in Biglan et al.’s (2013) study, the primary focus of the study’s intervention, Acceptance and Commitment therapy (ACT), was to recognize and accept negative thoughts and feelings as a necessary step towards diffusing them and responding effectively to stressful situations. In diffusing these thoughts, teachers were reminded to remain steadfast to teaching values that they deemed important as they interacted with their students (Biglan et al., 2013).

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Cooley and Yovanoff (1996) implemented two interventions to address special education teachers’ stress; a stress-management intervention and a peer collaboration program. In addition to fostering cognitive coping skills as did the other two studies, during Cooley and Yovanoff (1996)’s stress-management intervention teachers were trained in situational coping skills as well as physiological coping skills. Workshops on situational coping skills pertained helping teachers identify aspects of stressful situations that could be changed and think about potential solutions they could implement. Trainings on physiological coping skills equipped teachers with practices that improved how their bodies reacted to stress to help them relax. Physiological coping skills included practicing muscle relaxation and stretching as well as addressing nutrition. In the peer collaboration intervention program, teachers were paired together and trained to carry out a fourstep process to practice effective communication and support-giving techniques (Cooley & Yovanoff, 1996). Quality of Studies All three studies conducted randomized control trials and thus randomly assigned participants to their treatment and control groups. The three studies also collected follow-up data and were, therefore, able to track intervention effects two months (Benn et al., 2012), six months and oneIt year (Cooley & Yovanoff, 1996), and two years (Biglan et al., 2013) after concluding the intervention. Moreover, they all had waitlist control groups which ensured that all participants were able to benefit from the interventions and learn skills that helped them reduce their stress and improve their overall well-being. In addition, all three studies provided detailed descriptions of the interventions they implemented, describing the sessions conducted, the activities practiced during those sessions, and the approximate durations of the sessions. It is worth noting some concerns related to how the studies were conducted. Some of the sampling and randomization procedures across the studies could have led to the introduction of possible bias. High attrition of participants after they were randomly assigned to control and treatment groups resulted in smaller sample sizes which, in turn, prevented Benn et al. (2012) from conducting statistical analyses on certain follow-up measures. In addition, high attrition caused the make-up of the control and treatment groups to vary. Participants who only completed pre-intervention surveys were found to be more depressed and participants who did not complete follow-up surveys were found to score higher on stress and anxiety levels (Benn et al., 2012). Those differences in the nature of participants between the groups renders it necessary to exercise caution when interpreting this review’s findings due to possible bias. Biglan et al.’s (2013) randomization procedures might have also introduced bias since they randomly assigned teams rather than individuals to control and treatment group- a feature of the study they mentioned not taking into account when conducting their statistical analysis. This randomization technique could have, therefore, introduced bias at the group level since participants were placed together in treatment and waitlist control groups. In addition, since waitlist control groups eventually received the intervention, comparing the control and treatment groups was limited to the first post-intervention assessments after which any further comparisons could have been confounded with the introduction of the treatment condition to the wait-list control groups. Cooley and Yovanoff (1996) stated that this limitation is important to note since they expected that the nature of the intervention skills being taught

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required a long time for participants in the treatment groups to practice and integrate before more accurate results were produced. Findings Effectiveness of Programs The effectiveness of the interventions on teachers’ measures of stress varied across the studies (refer to Table 2). Variability in the significance of effectiveness also existed within the studies where specific stress-related measures were found to be significantly associated with the intervention while others were not. In addition, some effects did not appear immediately after the treatment condition but seemed to become larger and more apparent during follow-up. In general, all three studies illustrated that their interventions had positive results on measures of teachers’ stress levels and burnout. Specifically, the interventions implemented by Benn et al. (2012) and Biglan et al. (2013) improved measures of stress post-intervention for the treatment groups. Even though the intervention had positive effects on burnout, in Cooley and Yovanoff’s (1996) study, the effect was not statistically significant between the control and treatment groups post-intervention (between-group results). Significant improvement effects on emotional exhaustion and personal accomplishment, two components of Maslach’s measure of burnout, however, appeared for the treatment group when the authors conducted within-group-by-time analyses (Cooley & Yovanoff, 1996), indicating important interaction effects between group membership (treatment and control) and time (pre/post). Results of other stress-related measures were inconsistent across the studies. Benn et al.’s (2012) intervention did not have a significant effect on teaching efficacy while Biglan et al.’s (2013) intervention did. On the other hand, Benn et al.’s (2012) intervention significantly improved teachers’ reports of anxiety, depression, negative affect, and self-compassion post-intervention, yet it had no significant effect on teachers’ positive affect or forgiveness. Biglan et al. (2013) did not conduct analyses to investigate whether the intervention had an effect on burnout, job satisfaction, or depression. At follow-up, two months after the intervention, Benn et al. (2012) found that the intervention’s effect on depressive symptoms faded, while the effect sizes on perceived stress, negative affect, anxiety, and self-compassion became larger. Follow-up scores (taken six months and one year post intervention) in Cooley and Yovanoff’s (1996) study demonstrated a continued improvement in job satisfaction and emotional exhaustion while measures of depersonalization and personal accomplishment became worse. Biglan et al. (2013) collected open-ended responses for their follow-up investigation two years later and found that respondents noticed a sustained positive change in school climate where teachers became more open to feedback, colleagues were more supportive of one another, and that staff were more willing to try new strategies and programs to improve students’ behavioral and social skills. Social Validity Two out of the three studies examined the social validity of their interventions (Benn et al., 2012; Cooley and Yovanoff, 1996). Benn et al. (2012) did not explicitly mention social validity, but instead provided an examination of program feasibility which seemed to shed light on the social 42

validity of the program. They asked participants to report on how long they practiced the mindfulness techniques at home and how satisfied they were with the program. Participants in their study reported practicing the techniques at home on a daily basis, experiencing high satisfaction with the program, and recommending the training to their colleagues (Benn et al., 2012). Cooley and Yovanoff (1996) provided a clear analytical procedure for examining their study’s social validity which was based on quantitative and qualitative data collected soon after the completion of the intervention program. Participants were given a survey where they were asked to rate the importance of each intervention’s goal and rate how successful the intervention was in reaching each goal. Participants rated the interventions’ goals as moderately to highly important and successful, with slightly higher ratings given to the stress-management intervention program than to the peer collaboration program. Written comments that were also collected by Cooley and Yovanoff (1996) demonstrated highly positive experiences and usefulness of the training in helping teachers address work-related stress and avoid burn

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Table 2 Summary of Interventions’ Effects on Relevant Outcome Measures Significant Effects at Post-Intervention/Follow-up Constructs

Benn et al. (2012)

Biglan et al. (2013)

Cooley & Yovanoff (1996)

Stress

Yes/Yes

Yes/No

-

Depression

Yes/No

-

-

No/-

Yes/-

-

Job Satisfaction

-

No/-

Yes/Yes

Negative Affect

Yes/Yes

-

-

Anxiety

Yes/Yes

-

-

Self-Compassion

Yes/Yes

-

-

Forgiveness

No/Yes

-

-

Depersonalization

-

-

No/W.

Personal Accomplishment

-

-

Yes/W.

Emotional Exhaustion

-

-

Yes/Yes

Teaching Self-Efficacy

Burnout

Note. W = Effect worsened significantly, - = Not applicable.

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Discussion This review presented findings from three studies that have conducted stress-management interventions with special education teachers. The purpose of this review was to examine the types of interventions that were conducted to support special education teachers as they coped with job-related stressors and to explore what effect those interventions had on the teachers’ experiences of stress and well-being. Types of Interventions Used The main interventions that all three studies in this review incorporated could be described as acceptance-based. Acceptance-based interventions incorporate skills that enhance an individual’s awareness and acceptance of negative thoughts and feelings in order to become nonreactive to them (Benn et al., 2013; Biglan et al., 2012) or to simply dismiss them (Cooley & Yovanoff). Teachers’ positive perceptions of their own ability to meet job demands, their ability to engage in emotional regulation, and ability to address negative thoughts when responding to stressful situations have been identified as important components of effective stress-management strategies (Emery & Vandenberg, 2010; Kerr & Brown, 2016; Perry et al., 2015). Accepting negative thoughts and emotions, also known as habitual acceptance, has been found to be associated with improved psychological well-being, lower stress, lower depressive symptoms, and lower negative emotional responses to stress (Ford et al., 2018). Acceptance-based interventions that focus on improving an individual’s acceptance and non-judgement of their negative emotions and thoughts have, therefore, been found to hold promising possibilities in helping special education teachers cope with stress (Emery & Vandenberg, 2010). Examples of interventions that incorporate acceptance of negative thoughts and emotions include Dialectical Behavior Therapy (DBT; Perseius et al., 2007), Mindfulness-Based Stress Reduction program (MBSR; Baer et al., 2012), and Acceptance and Commitment Therapy (ACT; Pülschen & Pülschen, 2015). Stress-related aspects of well-being that the interventions seemed to target were mainly emotional and cognitive with only one study (Cooley & Yovanoff, 1996) incorporating physical care and well-being (such as nutrition, exercise, and stretching exercises). Stress is indeed a multifactorial experience which affects cognitive, emotional, and physiological aspects of an individual (Schneiderman et al., 2005). In fact, caretakers of students with disabilities have been found to suffer from an increase in physiological ailments as a result of experiencing higher stress levels while attending to a child with special needs (Gallagher & Whiteley, 2013). Giving teachers the skills to not only cope emotionally and cognitively but also physiologically might encourage more teachers to stay engaged such interventions which could explain why Cooley and Yovanoff (1996) had low participant attrition rates. This trend could point to the importance of providing teachers with a variety of options when delivering an intervention to encourage sustained engagement. Effectiveness of the Interventions Overall, all three studies demonstrated that their interventions were effective in improving some aspects of teachers’ well-being and stress, demonstrating a range of small to large effect sizes. Interestingly, in most of the studies the effect sizes of the intervention skills on stress and other measures of well-being tended to become larger over time after the intervention was completed, 45

resulting in larger effect sizes during follow-up (Benn et al., 2012; Cooley & Yovanoff, 1996). These results were intriguing and could be indicative of the dynamic and temporal nature of acceptance-based skills (Kuyken et al., 2010). It is possible that acceptance-based skills take time to be fully adopted and practiced by its practitioners. These skills have been shown to improve with time, leading to improved observation, awareness and nonreactivity to negative thoughts and feelings which in turn were found to be associated with improved perceptions of stress (Baer et al., 2012). Moreover, the effect of acceptance-based skills on practitioners’ well-being have been found to continue beyond the conclusion of an intervention which reflects the long-term effects that those skills could have on helping special education teachers cope with stress (Spinhoven et al., 2017). Contextual Influences on Interventions Several contextual factors seemed to influence the extent to which the stress-management interventions were successful. One such determining factor was the role that the schools played in their implementation of such interventions (Jennings & Slavin, 2015). Cooley & Yovanoff (1996) noted that individual-level interventions might not be supported in the long-run by the school at large, resulting in institutional-level challenges to teachers’ implementation of coping strategies. Implementing interventions at the school-level could ensure that school-level factors such as administrators (Westling et al., 2006) and colleagues (Jones et al., 2013) do not hinder teachers’ practicing of coping strategies when experiencing stress. Other possible contextual influences include time constraints and the demanding nature of special education teachers’ roles (Cancio et al., 2018; Grant, 2017; Leko & Smith, 2010). It is possible that high attrition rates of participants before the beginning of the interventions were a result of teachers’ inability to make substantial time commitments at the expense of work-related demands (Benn et al., 2012). Even though the research body about stress-management interventions for special education teachers is scant, acceptance-based interventions which were adapted for busy life-styles more generally and were thus shorter and briefer in duration were found to produce improved scores on a variety of stress-related measures of well-being (Cavanagh et al., 2013; Waters et al., 2018). Brief interventions which incorporate short exercises rather than more time-demanding ones could, therefore, prove more beneficial by encouraging a more sustained practice for busy special education teachers. Limitations The articles reviewed for this study had some noteworthy limitations. First, the articles did not provide a detailed description of the special education teachers who participated. Aggregated results might have masked nuanced effects that the interventions had on certain teachers. Given that certain characteristics such as experience level, specialty, and age have been shown to be associated with teachers’ varying levels of job commitment and burnout (Cancio et al., 2013; Conley & You, 2017), disaggregated descriptions of participants could have allowed us to understand more specifically why teachers varied in their responsiveness to the stressmanagement interventions. Aside from insufficient information about the participants, there was a lack of information provided about the settings where those interventions were implemented. Based on the extant literature on the discrepant experiences that teachers have in urban, high poverty, and high minority schools and programs (Billingsley et al., 2020), it is expected that the nature of the 46

stressors that those teachers confront and the nature of the resources made available to them would vary from other school settings. Sharing this information would have allowed for a more in-depth understanding of the differential effects stress-management interventions might have on special education teachers in different school environments. Additionally, little information was given about the studies’ use of implementation fidelity measures to ensure the interventions were implemented with credibility. Since many staff were involved in delivering the strategies to the teachers, deviation from the set instructions is inevitable if there was no method of ensuring standardization. Limitations of this literature review should also be noted. First, only three journals were hand searched for relevant articles to include in this review. A hand search of a larger number of journals from the fields of Psychology and Special Education could have yielded a larger selection of articles for this literature review. Secondly, this review was limited to studies that included special education teachers. Stress-management studies which were conducted with professionals who operate within similar contexts (for e.g., such as behavior analysts, speech language pathologists, or physical therapists attending to students with disabilities in educational institutions) might have shed light on other effective ways special education teachers could potentially be supported. Practical and Research Implications The positive results of all three studies generally demonstrate the potential that stressmanagement interventions hold in helping special education teachers cope with the daily stressors of working in the field. In light of these findings, we urge school leaders and teacher preparation leaders to provide current and pre-service special education teachers with opportunities to partake in stress-management programs that promote the practice of acceptancebased skills. Given special education teachers’ busy schedules, programs that offer brief sessions with flexible participation options (such as in-person, remote, or pre-recorded, for instance) could encourage teachers’ higher and more sustained attendance. Providing on-going emotional and professional support to special education teachers could also promote a work climate that bolsters their willingness to attend stress-management intervention sessions and continue practicing effective coping skills in the long term. In addition, and as noted earlier, the studies’ primary focus was on addressing stress at an individual-level, rather than at the systemic-level, driven by an understanding that the former is more conducive to immediate change and improvement. Even though generally positive, the varying effect sizes that these interventions had on teachers’ stress levels could be indicative of the limited influence that individual-level interventions have on stress within the larger systemic dynamics of the field (Billingsley et al., 2020; Lazarus & Feldman, 1984). In other words, these results point to the importance of recognizing the integral role that contextual elements play in affecting teachers’ capacities to cope with stress. Additional research is, therefore, needed to further examine teachers’ coping mechanisms in tandem with, rather than in isolation from, the contexts of their teaching environments. Future research studies on the effectiveness of stressmanagement programs in helping special education teachers cope should draw on cognitive, social, and ecological conceptual frameworks to produce more nuanced understandings of how teachers perceive, make sense of, and respond to stressors. Research which is embedded within such contextual frameworks could contribute more realistic implications on how stress47

management programs could be more responsive to special education teachers’ needs and, thus, more effective. References References marked with an asterisk were included in the literature review. Baer, R. A., Carmody, J., & Hunsinger, M. (2012). Weekly change in mindfulness and perceived stress in a mindfulness-based stress reduction program. Journal of Clinical Psychology, 68(7), 755–765. https://doi.org/10.1002/jclp.21865 *Benn, R., Akiva, T., Arel, S., & Roeser, R. W. (2012). Mindfulness training effects for parents and educators of children with special needs. Developmental Psychology, 48(5), 1476– 1487. https://doi.org/10.1037/a0027537 *Biglan, A., Layton, G. L., Jones, La. B., Hankins, M., & Rusby, J. C. (2013). The value of workshops on psychological flexibility for early childhood special education staff. Topics in Early Childhood Special Education, 32(4), 196–210. https://doi.org/10.1177/0271121411425191 Billingsley, B. & Bettini, E. (2019). Special education teacher attrition and retention: A review of the literature. Review of Educational Research, 89(5), 697-744. https://doi.org/10.3102/0034654319862495 Billingsley, B., Bettini, E., Mathews, H. M., & McLeskey, J. (2020). Improving working conditions to support special educators’ effectiveness: A call for leadership. Teacher Education and Special Education, 43(1), 7–27. https://doi.org/10.1177/0888406419880353 Brunsting, N. C., Sreckovic, M. A., & Lane, K. L. (2014). Special education teacher burnout: A synthesis of research from 1979 to 2013. Education and Treatment of Children, 37(4), 681–712. https://doi.org/10.1038/368057a0 Cancio, E. J., Albrecht, S. F., & Johns, B. H. (2013). Defining administrative support and its relationship to the attrition of teachers of students with emotional and behavioral disorders. Education and Treatment of Children, 36(4), 71–94. https://doi.org/10.1353/etc.2013.0035 Cancio, E. J., Larsen, R., Mathur, S. R., Estes, M. B., Johns, B., & Chang, M. (2018). Special education teacher stress: Coping strategies. Education and Treatment of Children, 41(4), 457–481. https://doi.org/10.1353/etc.2018.0025 Cavanagh, K., Strauss, C., Cicconi, F., Griffiths, N., Wyper, A., & Jones, F. (2013). A randomised controlled trial of a brief online mindfulness-based intervention. Behaviour Research and Therapy, 51(9), 573–578. https://doi.org/10.1016/j.brat.2013.06.003 Conley, S., & You, S. (2017). Key influences on special education teachers’ intentions to leave: The effects of administrative support and teacher team efficacy in a mediational model. Educational Management Administration and Leadership, 45(3), 521–540. https://doi.org/10.1177/1741143215608859 *Cooley, E., & Yovanoff, P. (1996). Supporting professionals-at-risk : Evaluating interventions to reduce burnout and improve retention of special educators. Exceptional Children, 62(4), 336–355. https://doi.org/10.1177/001440299606200404 Dicke, T., Stebner, F., Linninger, C., Kunter, M., & Leutner, D. (2018). A longitudinal study of teachers’ occupational well-being: Applying the job demands-resources model. Journal of Occupational Health Psychology, 23(2), 262–277. https://doi.org/10.1037/ocp0000070 48

Emery, D. W., & Vandenberg, B. (2010). Special education teacher burnout and ACT. International Journal of Special Education, 25(3), 119–131. Retrieved from https://eric.ed.gov/?id=EJ909042 Ford, B. Q., Lam, P., John, O. P., & Mauss, I. B. (2018). The psychological health benefits of accepting negative emotions and thoughts: Laboratory, diary, and longitudinal evidence. Journal of Personality and Social Psychology, 115(6), 1075–1092. https://doi.org/10.1037/pspp0000157 Gallagher, S., & Whiteley, J. (2013). The association between stress and physical health in parents caring for children with intellectual disabilities is moderated by children’s challenging behaviours. Journal of Health Psychology, 18(9), 1220–1231. https://doi.org/10.1177/1359105312464672 Grant, M. C. (2017). A case study of factors that influenced the attrition or retention of two firstyear special education teachers. Journal of the American Academy of Special Education Professionals, (January), 77–84. Retrieved from http://aasep.org/aaseppublications/journal-of-the-american-academy-of-special-education-professionals-jaasep Jennings, M. L., & Slavin, S. J. (2015). Resident wellness matters: Optimizing resident education and wellness through the learning environment. Academic Medicine, 90(9), 1246–1250. https://doi.org/10.1097/ACM.0000000000000842 Jones, N., Youngs, P., & Frank, K. (2013). The role of school-based colleagues in shaping the commitment of novice special and general education teachers. Exceptional Children, 79(3), 365–383. https://doi.org/10.1177/001440291307900303 Klusmann, U., Richter, D., & Ludtke, O. (2016). Teachers’ emotional exhaustion is negatively related to students’ achievement: Evidence from a large-scale assessment study. Journal of Educational Psychology, 108(8), 1193–1203. https://doi.org/10.1037/edu0000125 Kuyken, W., Watkins, E., Holden, E., White, K., Taylor, R. S., Byford, S., Evans, A., Radford, S., Teasdale, J. D., & Dalgleish, T. (2010). How does mindfulness-based cognitive therapy work? Behaviour Research and Therapy, 48(11), 1105–1112. https://doi.org/10.1016/j.brat.2010.08.003 Leko, M. M., & Smith, S. W. (2010). Retaining beginning special educators: What should administrators know and do? Intervention in School and Clinic, 45(5), 321–325. https://doi.org/10.1177/1053451209353441 Maslach, C. (2003). Job burnout: New directions in research and intervention. Current Directions in Psychological Science, 12(5), 189–192. https://doi.org/10.1111/14678721.01258 Maslach, C. & Leiter, M. P. (2016). Understanding the burnout experience: recent research and its implications for psychiatry. World Psychiatry, 15(2), 103-111. https://doi.org/10.1002/wps.20311 Paquette, K. R., & Rieg, S. A. (2016). Stressors and coping strategies through the lens of early childhood special education pre-service teachers. Teaching and Teacher Education, 57, 51–58. https://doi.org/10.1016/j.tate.2016.03.009 Perry, N. E., Brenner, C. A., Collie, R. J., & Hofer, G. (2015). Thriving on challenge: Examining one teacher’s view on sources of support for motivation and well-being. Exceptionality Education International, 25(1), 6–34. https://doi.org/10.5206/eei.v25i1.7715 Perseius, K. I., Kåver, A., Ekdahl, S., Åsberg, M., & Samuelsson, M. (2007). Stress and burnout in psychiatric professionals when starting to use dialectical behavioural therapy in the work with young self-harming women showing borderline personality symptoms. 49

Journal of Psychiatric and Mental Health Nursing, 14(7), 635–643. https://doi.org/10.1111/j.1365-2850.2007.01146.x Pülschen, S., & Pülschen, D. (2015). Preparation for teacher collaboration in inclusive classrooms – stress reduction for special education students via acceptance and commitment training: A controlled study. Journal of Molecular Psychiatry, 3(1), 1–14. https://doi.org/10.1186/s40303-015-0015-3 Schneiderman, N., Ironson, G., & Siegel, S. D. (2005). Stress and health: Psychological, behavioral, and biological determinants. Annual Review of Clinical Psychology, 1(1), 607–628. https://doi.org/10.1146/annurev.clinpsy.1.102803.144141 Spinhoven, P., Huijbers, M. J., Ormel, J., & Speckens, A. E. M. (2017). Improvement of mindfulness skills during Mindfulness-Based Cognitive Therapy predicts long-term reductions of neuroticism in persons with recurrent depression in remission. Journal of Affective Disorders, 213(September 2016), 112–117. https://doi.org/10.1016/j.jad.2017.02.011 Waters, C. S., Frude, N., Flaxman, P. E., & Boyd, J. (2018). Acceptance and commitment therapy (ACT) for clinically distressed health care workers: Waitlist-controlled evaluation of an ACT workshop in a routine practice setting. British Journal of Clinical Psychology, 57(1), 82–98. https://doi.org/10.1111/bjc.12155 Westling, D. L., Herzog, M. J., Cooper-Duffy, K., Prohn, K., & Ray, M. (2006). The teacher support program: A proposed resource for the special education profession and an initial validation. Remedial and Special Education, 27(3), 136–147. https://doi.org/10.1177/07419325060270030201 Wisniewski, L., & Gargiulo, R. M. (1997). Occupational stress and burnout among special educators: A review of the literature. Journal of Special Education, 31(3), 325–346. https://doi.org/10.1177/002246699703100303 About the Authors Haya Abdellatif, M.S., M.Ed. is a Ph.D. candidate in the Urban Special Education Scholar Program at the University of Pittsburgh. Her research interests include using psychosocial and ecological frameworks to explore ways to support students with or at risk for emotional and behavioral disorders, their teachers, and their families. Rachel Robertson, Ph.D. is an assistant professor of special education at the University of Pittsburgh where she teaches and conducts research on issues related to autism spectrum disorders, applied behavior analysis, and positive behavior support.

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Systematic Barriers to Voting for Adults with Disabilities Alena Bates, M.Ed., BCBA Texas State University Holly Collinsworth, M.Ed., BCBA University of North Texas Cara Speicher, B.A. Southern Methodist University Abstract The Americans with Disabilities Act (1990) provided individuals with disabilities many rights; including the right to vote. Voting is one of the most important civic duties afforded to citizens, and despite efforts to give access to this fundamental democratic process to all, not all individuals with disabilities can participate in the process. A qualitative systematic literature review was conducted to explore the research question: What experiences do individuals with disabilities face when participating in the democratic process of voting? A total of seven articles were found that fit within the inclusion/exclusion criteria and analyzed for emerging common themes within the University of North Texas library databases. Two overarching themes, education and voice were identified. The results of this literature review revealed that more research is needed to provide a voice to the opinions of adults with disabilities on voting and that specific instruction on voting is not being provided to adults with disabilities. Several implications from this literature review were identified. The information synthesized can guide support personnel and parents/guardians on how to provide assistance and support to adults with disabilities when voting and encourage researchers to develop voting instruction focusing on conceptual content and motivate policymakers to review current legislation to make voting more accessible to all eligible voters. Keywords: voting, disability, barriers, experiences, qualitative Systematic Barriers to Voting for Adults with Disabilities Securing the civil rights of individuals with disabilities as full participants in American society has been a long and arduous process. Voting is one of the most important civic duties Americans participate in to ensure that their ideas, beliefs, and priorities for their community are known by their representatives. Despite the effort to make this fundamental democratic process more accessible, individuals with disabilities are not full participants in the process. Five federal laws protect people with disabilities’ rights concerning voting. Accessibility and assistance at the polling station are provided for in the Voting Rights Act of 1956, the Voting Accessibility for the Elderly and Handicapped Act of 1984, and the Help America Vote Act of 2002. The Voting Rights Act of 1956, provides for voters to receive assistance at a polling station (U.S Department of Justice, 2014) and, the Voting Accessibility for the Elderly and Handicapped Act of 1984 requires accessible polling places (U.S Department of Justice, 2014). The Help America Vote Act of 2002 requires that polling stations have one accessible voting station for persons with 51

disabilities, and that station must provide an equal opportunity to voting like the other stations (U.S Department of Justice, 2014). In the 1990s, the voting rights of individuals with disabilities were more closely examined and spurned the Americans with Disabilities Act in 1990, which provided individuals with disabilities many rights; including the right to vote. In 1993 the National Voter Registration Act of 1993 required all state-funded institutions that serve individuals with disabilities the opportunity to register to vote (U.S Department of Justice, 2014). More recently, in 2019, the Individuals with Disabilities Education Act (IDEA (part B) recognized 14 categories of disabilities, and each category can affect the ability to vote (2019). Disabilities affect individuals’ rights to vote by creating challenges in accessing the voting process. Some of the challenges include lack of transportation to and from the polling places, lack of physical mobility, lack of training on how to vote, lack of valid I.D., as well as the inability to read the ballot independently. An intellectual disability is defined as “significant limitations in both intellectual functioning and in adaptive behavior, which covers many everyday social and practical skills” (American Association on Intellectual and Developmental Disabilities, 2020). Individuals with an intellectual disability face additional challenges when voting. Agran et al. (2005) stated, “individuals with intellectual disabilities may be prevented from voting because it is believed they are incapable of voting; that is, they are considered to be non-compos mentis (i.e., not of sound mind).” In the 2010 midterm elections, 45.9% of people without disabilities voted, and 42.8% of people with disabilities voted with a -3.1% disability turnout gap (Schur & Kruse, 2018). In the 2014 midterm election, 42.1% of people without disabilities voted and 40.8% of people with disabilities voted with a -1.3% voter turnout gap (Schur & Kruse, 2018). In the 2018 midterm election, 54% of people without disabilities voted and 49.3% of people with a disability voted with a -4.7% disability turnout gap (Schur & Kruse, 2018). Schur & Kruse (2018, p.2) stated, “turnout dropped slightly from 2010 to 2014 for people both with and without disabilities but increased markedly in 2018. The 2018 increase was greater for people without disabilities, so the overall disability turnout gap expanded in 2018.” Using their data, Schur and Kruse (2018) determined that there would be 2.35 million more voters if each group (with and without disability) voted at the same rate. These statistics demonstrate over time that people with disabilities do not vote at the same rate as citizens without a disability and in fact, their participation is reducing. Through a systematic meta-synthesis of research, we will explore the experiences and perceptions that individuals with disabilities encounter when voting. People with disabilities may feel powerless, defeated, and fearful when they cannot vote because voting is an expression of choosing a candidate that shares their values and beliefs. This question is more important than ever in American society as the country faced an unprecedented barrier to voting during an election year. The COVID-19 pandemic catalyzed the authors to explore the literature surrounding the barriers to voting with a disability. The rationale for a systematic literature review and synthesis is to explore and synthesize participation experiences in the democratic process for individuals with disabilities and make policy recommendations to ensure all eligible voters have access to the ballot. Methods Inclusion/Exclusion Criteria Table 1 outlines the process for determining the inclusion/exclusion criteria. The article search process started with limiting the search to only peer-reviewed articles found in online databases. 52

Next, to control for differences in policies and practices, only articles regarding the democratic processes in the United States were included in this systematic literature review. This also limited the search to articles written in English. To keep with the most current research, only articles published within the last 10 years were included in the systematic literature review. As we explore an aspect of society and human experiences, mixed methods and qualitative research articles were included. We excluded purely quantitative research articles from thematic analysis but included quantitative articles for background information into our research question. By choosing to open our search to both qualitative and mixed-method articles, we can pull in a variety of data sources, such as case studies, interviews, narratives, and surveys. We excluded purely quantitative data such as voter registry information as it would not open itself up to a thematic analysis. Articles were limited in topic and research questions as well. Studies that focused on individuals without disabilities or other barriers to voting that were unrelated to having a disability, such as cultural or socio-economic barriers, were excluded. Articles that explored barriers to voting in connection with a disability, voting rights of people with disabilities, and disenfranchisement as a result of having a disability were all included in the article search. Table 1 Inclusion/Exclusion Criteria Criteria

Include Studies Disability and: voter rights, disenfranchisement, barriers to voting

Exclude Studies Individuals that do not have a disability. Voters whose barrier to voting is not related to having a disability

Research question

Barriers to voting in connection with a disability

Barriers to voting unrelated to having a disability (cultural barriers, socio-economic barriers, etc.)

2010-2020

Articles outside of the 10-year time frame

Research design

Mixed methods, case studies, phenomenology

Purely quantitative

Included data

Interviews, coding of themes, narratives, surveys

Purely quantitative data

Publication outlet

Peer-reviewed articles in academic (education, special education, JABA, law, sociology, educational psychology) journals

Book chapters, newspaper articles, dissertations

Topic

Date

Search Strategy All library databases within the University of North Texas library system were used in searching for peer-reviewed research articles. The topic of barriers to voting for individuals with 53

disabilities spans many disciplines, leading to the decision to include all databases available in the University of North Texas Library database. Researchers began their search by typing “voting with disability” into the search bar with the parameters of peer-reviewed and full-text online enabled. The database results were 2,486 articles. Researchers only accepted articles from 2010-2020. This resulted in 1,604 articles. Parameters were set for articles in the areas of law, public health, political, education, government, rehabilitation, psychology, social science, social welfare, and sociology and 520 articles were included. Researchers excluded articles that were not in the English language because the researchers are not bilingual. Next, researchers added the term “barriers” to the search engine, and the results were narrowed down to 178 articles. All book reviews, newspaper articles, and dissertations were not included, and the articles remained at 178. Researchers reviewed abstracts and did not include research that was published outside of the United States of America and this resulted in 19 articles. Researchers did not include articles that had participants outside of the United States and 12 articles met these criteria. The full articles were independently reviewed by two researchers and interobserver agreement was 100% and three articles remained. Researchers reviewed the reference section of the three qualifying articles for additional literature. 18 additional articles were found based on the inclusion/exclusion criteria, totaling 21 articles. 14 articles were not included because they fell outside of the 10-year inclusion criteria. As a result, a rich variety of research articles are included in this systematic meta-synthesis literature review of seven articles. See Figure 1 for a precise overview of the search strategy and process.

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Figure 1. Search process

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Data collection/analysis methods A spreadsheet was created to track articles and organize them for comparison. The components of each research article tracked were: authors, research design, participants, settings, target skills, interobserver agreement for dependent variables, intervention components and density, procedural fidelity, social validity measures, effect size methods (when reported), and reported weakness of the study. Prior to coding, the seven articles that met the criteria were subjected to a quality analysis using the eight indicators of quality qualitative research as outlined by Tracy in “Qualitative quality: Eight “Big-tent” criteria for excellent qualitative research” (2010). The Tracy (2010) indicators guided assessment of the quality of qualitative research based on a worthy topic, rich rigor, sincerity, credibility, resonance, significant contribution, ethics, and meaningful coherence. This information is organized in a table for comparison. The seven articles within this meta-synthesis contained the following indicators of quality research; 1) worthy topic, 2) credibility, 3) ethics, 4) sincerity and 5) meaningful coherence. Table 2 Article Comparison and Quality Assessment Article comparison

Research Question/Purpose

Sample

Setting

Method of Analysis

Conclusion

Quality Assessment

Agran & Hughes (2013)

What is the value of voting to people with an intellectual or developmental disability and what instruction have they had on voting?

100 support personnel

Western United States

Responses analyzed by frequency and descriptively

Few clients are registered to vote or receive instruction in voting.

Worthy topic, credibility, ethical, sincerity, and meaningful coherence

Agran et al. (2016)

Purpose: To obtain input from people with ID regarding opinions and experiences regarding voting.

28 adults with ID

Rocky Mountain region of the Western United States

Thematic analysis using constant comparison and content analysis

Voting is important to the majority of adults with ID, over half of the participants had received preparation to vote, and with encouragement and support participants are more likely to vote.

Worthy topic, credibility, ethical, sincerity, and meaningful coherence

Agran et al. (2015)

Expanded upon Agran & Hughes (2013) study.

66 support personnel

Support personnel: case managers, parents, and

Grounded theory analysis, constant comparison, and descriptive statistics

People with disabilities want to vote and are registered but have not been

Worthy topic, credibility, ethical, sincerity, and

56

disability organizations

provided instructions on the process.

meaningful coherence

Agran et al. (2020)

Purpose: To share opinions and experiences of people with disabilities.

Direct support staff for individuals with intellectual disabilities

Wyoming, United States of America

Survey that analyzed Likert-scale, multiple choice, and openended items

Clients were not registered to vote, had expressed little interest in voting, and had not received voting-related instruction.

Worthy topic, credibility, ethical, sincerity, and meaningful coherence

Kingston (2014)

Purpose: To use an interpretive stance to describe voting rights for individuals with disabilities and barriers.

Commentary

Commentary

Commentary and interpretive analysis

The participation in voting shows the gap and how adults with disabilities fundamental rights are threatened.

Worthy topic, credibility, ethical, and meaningful coherence

Matsubayashi & Ueda (2014)

Purpose: To examine trends in self-reported voting rates among people with and without disabilities and determine policy changes.

Survey participants with and without a disability

Data from all 50 states and the District of Columbia

Analyzed through coding CPS data from 1980-2008

Despite federal and state laws to remove barriers to voting for individuals with disabilities, voting gaps still exist.

Worthy topic, credibility, ethical, sincerity, and meaningful coherence

Schur and Adya (2013)

Purpose: To examine whether people with disabilities are part of the political mainstream or remain outsiders.

2008 and 2010 CPS data, 2006 GSS data, and 2007 Maxwell poll participants

United States of America

Analyzed CPS data from 2008 and 2010, 2006 GSS, and 2007 Maxwell poll

Individuals with disabilities are less likely to vote than peers without disability.

Worthy topic, credibility, ethical, sincerity, and meaningful coherence

Coding protocol Once a quality analysis of the articles was completed they were subjected to a coding protocol. The coding scheme was multitiered. The first tier utilized conceptual thematic coding. Several themes were overt in the articles and could be extracted straight from the data. For example, the value of voting was specified in three articles (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016). The second coding tier utilized interpretive coding. Studies that reported the perspectives of participants with disabilities required interpretive coding as they required a lineby-line analysis of interview responses (Agran et al., 2016). 57

The analysis and synthesis process for this systematic literature utilized the review and synthesis process for meta-synthesis research as outlined by Major and Savin-Baden (2010). During this process, the articles were analyzed for first, second, and third-order themes to guide our discussion, draw our conclusions, and make recommendations for future directions in research and policy. While first-order themes were overt, second-order themes required deeper analysis. In the final step, third-order themes emerged as second-order themes were concentrated and synthesized to create overarching themes. These themes are examined from first to third order themes in the discussion section. Results Quality Analysis Worthy Topic. Voting rights is an especially timely topic in light of the recent presidential election in the United States. Tracy (2010) defined “worthy topic”, within qualitative research as “relevant, timely, significant, interesting, or evocative” (p. 840). All seven articles (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013) in this meta-synthesis met the criterion as a “worthy topic”. As the push for every voice to be heard and every vote to be counted gains momentum, it is important to ensure that no stakeholder is excluded. Credibility. According to Tracy (2010) “credibility” refers to the “trustworthiness, verisimilitude, and plausibility” of the researcher’s findings (p. 842). Credibility has several important dimensions, “thick description, triangulation, multivocality, and member reflections” (Tracy, 2010 p. 840). All seven articles in this meta-synthesis met the criteria for “credibility” within at least one of these dimensions: (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013). All seven of the articles met the criteria within the dimension of “thick description” which Tracy (2010) identifies as “one of the most important means of achieving credibility” (p. 843). The problem statement, the purpose of research, and interview descriptions and findings areas of the articles provided rich detail and in-depth depictions that provided readers with enough information to make decisions and provided contemplative fodder. Six articles (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Matsubayashi & Ueda, 2014; Schur & Adya, 2013) met the criteria for credibility through the use of a multitude of voices in research, multivocality. These articles achieved multivocality as they created a conversation regarding voting with a disability that included individuals with disabilities, their families, and professionals in the field. The Kingston (2014) article was the only exception to multivocality as it was a commentary paper. Ethics. The next quality indicator that was examined was ethics. Ethical research is a multifaceted indicator of quality and includes “procedural, situational, relational, and exiting ethics” (Tracy, 2010, p. 847). Procedural ethics refers to universal safeguards for the participants as set forth by governing bodies (institutions, universities, and government bodies) as well as encompassing the accuracy of reporting required of academic researchers. Situational ethics is not as dichotomous as procedural ethics. These practices emerge during the context of research and require constant reflective practices as responsible researchers. Relational ethics is similar to situational ethics in that constant reflective practices are required during the research practice; however, relational ethics is more personal as it “recognizes and values mutual respect, dignity, 58

and connectedness between researcher and researched” (Tracy, 2010, p. 847). All seven articles, (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013), met the ethics criterion. Sincerity. Sincerity, as defined by Tracy (2010), relates to “notions of authenticity and genuineness” (p. 841). Sincerity consists of two parts that focus on the researchers. The study is characterized by self-reflexivity of the researchers and transparency in methods and limitations. Six articles in this review, (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Matsubayashi & Ueda, 2014; Schur & Adya, 2013), met the criteria for transparency in methods. The Kingston (2014) article was the only exception to sincerity as it was a commentary paper. Five articles were transparent in the limitations of their study, (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Schur & Adya, 2013), with Matsubayashi & Ueda (2014) and Kingston (2014) being the two exceptions. No articles practiced self-reflexivity in the form of stating the positionality or bias of the researchers. Meaningful Coherence. Meaningful coherence is a multifaceted indicator of quality. Tracy (2010) states that meaningfully coherent studies garner plausibility because they “accomplish what they espouse to be about” (p. 848). Every aspect of the study must seamlessly connect. The stated purpose of the study will connect to the research design, which in turn will connect to the methods, and then connect to the analysis. This interconnectedness is grounded in the literature itself. All seven articles, (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013), met the criteria for meaningful coherence. Please see Table 2 for article comparison and quality assessment. Themes Seven research articles, (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020; Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013), met the inclusion/exclusion criteria and were subjected to a meta-synthesis that identified first-, secondand third-order themes. Seven first-order themes, five second-order themes, and four third-order themes were established. Two overarching themes developed from the synthesis process and are defined and explored. First-order themes were overt and present within the body of the seven articles reviewed. The themes did not require deep analysis as they were explicitly stated within the body of the articles. The seven first-order themes revolved around dimensions of voting: 1) the value of voting, 2) voting rights, 3) voting instruction, 4) support to vote, 5) voting gap, 6) inaccessibility and 7) voting with a specific type of disability. Four articles (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Schur & Adya, 2013) contained the theme of the value of voting. Participants in the study found there to be significance or non-importance in the act of voting influencing the value of voting. Five of the seven articles (Agran & Hughes, 2013; Agran et al., 2015; Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013) contained the theme of voting rights in reference to individuals with disabilities being allowed to vote or not vote, regarding current laws. Voting instruction was the third common theme found within four articles (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016; Agran et al., 2020), and whether adults with disabilities were provided voting instruction was a question asked in each of these articles. In three articles (Agran et al., 2015; Agran et al., 2016; Schur & Adya, 2013) the theme of support to vote was present. Support in this regard referred to the level of help or 59

accommodations that support personnel or parents/guardians provided to the individuals with disabilities to aid the person participating in the democratic process. Three of the seven articles (Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013) contained the theme of the voting gap. The voting gap referred to the discrepancy between voter participation in elections among people without a disability and people with a disability. The theme of inaccessibility emerged in three articles (Agran et al., 2020; Kingston, 2014; Schur & Adya, 2013). Inaccessibility referred to the physical ability to register to vote, transport to the polling center, and accessibility of the polling center. The final theme that emerged pertained to specific disability categories that discussed voting with a specific type of disability (Matsubayashi & Ueda, 2014; Schur & Adya, 2013). Specific disability categories dealt with the challenges that individuals face that relate to their disability. While several themes were shared between articles voting participation by disability was the least common theme and specific voting rights was the most common theme. See Table 3 for a summary of first-order themes. Table 3 First Order Themes Article

Value of Voting

Voting Rights

Voting Instruction

Support

Voting Gap

Inaccessibility

Agran & Hughes (2013)

Yes

Yes

Yes

No

No

No

Disability Category Participation No

Agran et al. (2016)

Yes

No

Yes

Yes

No

No

No

Agran et al. (2015)

Yes

Yes

Yes

Yes

No

No

No

Agran et al. (2020)

No

No

Yes

No

No

No

No

Kingston (2014)

No

Yes

No

No

Yes

Yes

No

Matsubayashi & Ueda (2014)

No

Yes

No

No

Yes

No

Yes

Schur & Adya (2013)

Yes

Yes

No

Yes

Yes

Yes

Yes

Five second-order themes emerged when examining first-order themes: 1) barriers, 2) education and training, 3) solutions, 4) point of view and 5) ableism. Barriers to voting presented themselves in many forms and levels. At the societal level, Kingston (2014) provided a history of legal barriers that had to first be overcome to secure voting rights for people with disabilities. Agran & Hughes (2013) discussed the legal right to vote as citizens were denied based on disability and assumed incompetence. Assumed incompetence made voting inaccessible at the individual level. Inaccessibility as a result of physical barriers was present in six out of seven articles except for Agran et al. (2016). Agran et al. (2016) discussed the inaccessibility of the information on specific political issues and candidates. 60

Logically following the idea of restricted access to information was the next theme: education and training. Education appeared in six out of seven articles with the exception being Matsubayashi & Ueda (2014). Lack of voting education was discussed in three articles (Agran & Hughes, 2013; Agran et al., 2015, Agran et al., 2020). There were different reasons for the lack of voting education such as the perception that it was not a desired skill (Agran & Hughes, 2013), it was not an important skill (Agran et al., 2020), or it was not a skill that should be taught to a person with an intellectual disability (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016, Agran et al., 2020; Kingston, 2014; Schur & Adya, 2013). Despite these archaic beliefs, education and training is especially important in voter participation, as Schur & Adya (2013) found a correlation between voter participation and education. Solutions for these problems were identified as the next theme. Education and training-based solutions to increase voter participation amongst individuals with disabilities included adding voting skills to service plans (Agran & Hughes, 2013; Agran et al., 2020), providing training and support to individuals so that they may access voting (Agran & Hughes, 2013; Agran et al., 2016, Agran et al., 2015, Agran et al., 2020), and training caregivers and educators on supporting voting (Agran et al., 2020). Current solutions identified were primarily law-based solutions to ensure that polling places and ballots are accessible and voting rights of individuals with disabilities are protected under federal law (Kingston, 2014; Matsubayashi & Ueda, 2014; Schur & Adya, 2013). An underlying second-order theme that emerged when examining first-order themes was the positionality of the participants. Except for Agran et al. (2016) and Matsubayashi & Ueda (2014), individuals with disabilities were absent from the conversation on voting and had no vocal point of view. A majority of the articles were from the perspective of researchers, caregivers, and support personnel (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2020; Kingston, 2014; Schur & Adya, 2013). While caregivers and educators reported that voting was not an important goal or skill that should be taught (Agran & Hughes, 2013; Agran et al., 2015; Agran et al., 2016, Agran et al., 2020; Kingston, 2014; Schur & Adya, 2013); individuals with disabilities, the most important stakeholder in the conversation, reported a desire to vote (Agran et al., 2016). The theme of ableism emerged as a result of the withholding of the voice of individuals with disabilities in the voting conversation. Ableism is a type of prejudice and discrimination of a person or group of people based on perceived ability as a result of disability. It silences those with a disability in favor of able-bodied individuals. Ableism emerged as a theme as the perceptions of caregivers and educators were examined in the second-order themes of inaccessibility, training and education, solutions, and participants’ point of view. Despite people with disabilities stating, “My voice counts too,” the response from caregivers and educators had been “I never thought about it” (Agran et al., 2016). See Table 4 for a visual summary of secondorder themes. Table 4 Second Order Themes Article Theme

Inaccessibility

Training/Education

61

Solution

Ableism

Point of View

Agran & Hughes (2013)

Legal, physical, communication, and peers without disability

Lack of voting education and goals

Include voting in service plans, conduct more research about the involvement of people with disabilities voting, and provide unbiased instruction on voting.

Perception that people with disabilities do not care about voting. People with disabilities are not morally fit to vote, cannot be trusted to vote, and their participation is unnecessary

Support personnel for people with disabilities

Agran et al. (2016)

Lack of education on political issues and lack of knowledge on each candidate

Lack of knowledge of the candidates running for office

Provide training on candidates, supportive environment, and assistance with voting.

People with disabilities vote based on arbitrary reasons

Adults with a disability

Agran et al. (2015)

Lack of support and lack of interest

Lack of voting education

Provide transportation of groups to polling places and support in registering to vote.

“Lack of ability” to vote

Support personnel for people with disabilities

Agran et al. (2020)

Lack of instruction, perceived of lack of interest, lack of interest from parent/guardian, no transportation to polling places, and accessibility of polling booths

Lack of accessible learning supports for instruction on voting, political issues, and candidate positions

Teach and support clients on registering to vote, how to determine who to vote for, and programmatic engagement. Include voting as a goal in service plans.

More important skills to teach. Cannot make an informed decision. Assumed incompetence

Support personnel for people with disabilities

Kingston (2014)

Physical and legal

Lack of clarity within federal laws and policies, lack of research for accessibility when voting, and lack of perspective from the disabled community

Laws to protect voter rights. More research conducted that includes perspectives from the disabled community.

Stereotypes. People with disabilities not in the conversation

Researcher

Matsubayashi & Ueda (2014)

Type of disability determines barriers. Lack of physical accessibility at

-

Policy implementation . Polling places physically

People with severe impairments are

People with and without a disability

62

polls. Using surveys may be a barrier

Schur & Adya (2013)

Type of disability, physical barriers, isolation, and economic resources

Correlation between education and likelihood to vote.

accessible and training for staff on assisting a person with cognitive impairment.

excluded from these surveys

Make voting accessible: physically, simplify forms, and provide mail in ballot.

Family is “informal gatekeeping,” and lack of capacity

People with and without a disability. (Deaf identity/Deaf culture are exceptions to the gap)

Through the concentration and synthesis of second-order themes, four third-order themes emerged: 1) systematic societal barriers, 2) procedural training and education, 3) societal perspectives, and 4) law-based solutions. The goal was not to simplify the themes but to find the essence of their connection. See Figure 2 for a visual summary of the process of the third-order theme. Systematic societal barriers are the barriers that exist at the very core of the larger society that individuals with disabilities live in and are widespread. The second-order theme of barriers was expanded in the third-order theme process to encompass the magnitude of the issue. These systematic societal barriers led to the voting gap, which was uncovered in the first-order themes. The systematic societal barriers range from the unintentional, such as failure to make polling places accessible, to the intentional, such as deciding that an individual is not competent to exercise their right to vote. Systematic societal barriers are the hardest to overcome as they are ingrained in a long tradition of status quo. While it is relatively easy to add a ramp to a polling place or modify a form to make it accessible, it is harder to change the belief system of a society. The third-order theme of societal perspectives is a convergence of the second-order themes of ableism and point of view. Multiple perspectives exist and interact within society. Perspectives do not exist in a vacuum and have real-world implications. Ableist beliefs and perspectives from individuals without disabilities are in direct conflict with self-actualized perspectives from individuals with disabilities. While it is not uncommon for different perspectives to conflict with one another there is a power imbalance between these differing perspectives. It is not a matter of simply saying “let’s agree to disagree” when one group’s perspective has a greater influence in the decision-making processes. The third-order theme of procedural training and education is a convergence of the second-order themes of solution and training and education. Providing training and education on voting rights and procedures is a partial solution. Here it is important to stress that this theme is about procedural training and education, which in and of itself is not a complete solution. Procedural training and education, described throughout the articles with training and education as a firstorder theme, only tells an individual how to vote. This type of training and education does not lead to higher-level decision-making skills. So, while procedural training and education are present, conceptual training and education are notably absent. 63

Law-based solutions to voter participation is the most widespread solution as it occurs at the federal level. This third-order theme requires separation from the other third-order themes as it is the only theme that undeniably leads to the right to vote for individuals with disabilities. Lawbased solutions came in the form of voting rights legislation starting in 1954 and the Americans with Disabilities Act of 1990 as the capstone to remove physical barriers as well. See Figure 2 for a visual summary of the process of the third-order theme.

64

Figure 2. Summary of the process of the third-order theme Through the synthesis of themes present in the seven articles, two overarching themes emerged. The first of these was that of divergent voices. Divergent voices encompassed the themes of systematic societal barriers, societal perspectives, and law-based solutions. Several voices emerged throughout the seven articles, with some voices converging in purpose. Those individuals with disabilities and advocates represented one voice in the desire to promote voter participation amongst individuals with disabilities. Another voice (that of educators and caregivers) examined their perspective as outsiders of the disability community. These two voices took a dichotomous direction from one another on a perspective continuum. 65

The two voices represent the main point of divergence: the voices of those with a disability versus those without a disability. While individuals with disabilities stated a desire to vote or an interest in the process, caregivers and educators did not see the same value. The voices of caregivers and educators combined with societal expectations that individuals with disabilities do not value voting stood in contrast to those explicitly stating a desire to participate in the democratic process. The voices of advocates were agents of change that catalyzed law-based solutions to stop the disenfranchisement of individuals with disabilities (Kingston, 2014). Change does not happen unless someone raises their voice. The second overarching theme to emerge was that of education. Education is both an institution of society and a process. Education encompasses the themes of systematic societal barriers, procedural training and education, and societal perspectives. While several articles showed evidence of individuals with disabilities receiving training or education regarding voting, this education has been procedural and not conceptual content. Individuals have been taught how to vote, but not how to decide who to vote for or how to dissect political commentary to find where their interests are best considered in the political sphere. In some instances, teaching an individual with a disability how to vote was not considered an essential goal. With or without intention, the failure to provide an adequate civics education is a systematic societal barrier as the decision on what we educate our citizens on in the public domain of formal education comes from the top down. This overarching theme of education extends to the need to educate all stakeholders on voting rights for people with disabilities. Individuals with disabilities are not the only ones to benefit from their participation in the democratic process and therefore should not be the only group to receive education on disability. Families, educators, and lawmakers need to be educated on the benefits of eligible voters participating in the process. This is especially advantageous to lawmakers as disability rights impact many stakeholders. People with disabilities may even be a voting bloc, making securing their vote especially valuable to politicians. Change comes from educating others, not just those with a disability. Discussion The results indicate that the voice of individuals with disabilities needs to be included in research and education provided to caregivers and adults with disabilities in order for them to feel confident to participate in the democratic process. Patterns were discovered between Agran et al. (2015), Matsubayashi & Ueda (2014), and Schur & Adya (2013) in their recommendations to provide a more accessible voting experience. Relationships between Agran & Hughes (2013), Agran et al. (2016), and Agran et al. (2020) demonstrated individuals with disabilities need to have voting goals and instruction included in their service plan. Kingston (2014) focused on the direction of future research in the areas of law and special education. The results of this literature synthesis allow the authors to provide the limitations of their study, recommendations, and areas of future research. Limitations There are several limitations of this meta-synthesis. The first is that there are only seven articles included in this review. One of the articles is a commentary piece and four of our articles have the same first author. When a first author cites their research, it can create a conflict of interest and question if the author can remain objective. The second limitation is that the authors of this 66

meta-synthesis scope of practice is within educational psychology. There were many research articles published about barriers for individuals with a disability experience when voting; however, the authors focused their research through a legal lens and not through a societal lens. The third limitation is that there is not enough experimental research in this field and the majority of the research does not voice the opinions of people with disabilities. Recommendations To address the experiences of individuals with disabilities that contribute to the voting gap, the authors have synthesized the recommendations from each article and provided specific information on how to implement the recommendations. How can support personnel encourage and assist adults with disabilities to vote? Support services and parents/guardians should encourage, support, and assist adults with disabilities to vote. To support adults with disabilities and decrease the voting gap is to ensure that voting is included as a goal in their service plan. Agran & Hughes (2013), Agran et al., (2015), Agran et al. (2016), and Agran et al. (2020) all discuss that service providers need to provide instruction to adults with disabilities. Participants within each of these articles provide procedural instruction to adults with disabilities but it is not conceptual instruction. From the perspective of adults with disabilities in Agran et al. (2016), clients expressed interest in wanting instruction on how to make an educated vote by disseminating values and policies between candidates. The participants in the Agran et al. (2016) article said that they are given minimal instruction in the procedural steps of voting. To achieve the goal, unbiased procedural and conceptual instruction should be provided. The instruction on conceptual concepts needs to teach how to disperse the candidates by political values and policies. Balanced, unbiased iconographs that break down the candidate’s positions in an easy to follow format would assist in this instruction. Support personnel and parents/guardians could work with candidates and local organizations such as the Intellectual and Developmental Disabilities (IDD) Council, or the League of Women Voters, to make user-friendly and accessible pamphlets and flyers. Conceptual instruction could be included in an individual's transition supplement/plans and service plan while they are still in high school or during adult transition services to the age of 22. The next step is to assist with registering to vote and the amount of support depends on the individual. Support personnel and parents/guardians could also work with local candidates and local organizations to organize drives to the polls for individuals with physical barriers. When arriving at the voting center, support personnel and parents/guardians can assist in the voting booth. Support can be provided by reading the instructions on the ballet, names of the candidates, or by physically assisting an individual with limited mobility. Employees and volunteers at the voting center must understand the rights of individuals with intellectual disabilities for individuals with intellectual disabilities to be able to exercise their right to vote. Support personnel and parents/guardians can provide transportation to and from the polling places. Kingston (2014) determined that the biggest voting gap is between able-bodied individuals and individuals with mobility disabilities. In a group home or residential setting, support personnel can transport groups to vote to be efficient. By making these instructional changes and physical accommodations we believe that adults with disabilities will be able to participate in the democratic process through voting. 67

How can laws change to make voting more accessible for adults with disabilities? Federal laws to protect voter rights are in place; however, more research needs to be conducted to determine why the laws are not aligning with the needs of voters with disabilities. Kingston (2014) stated, “46 percent of polling places had an accessible voting system that could pose a challenge to certain voters with disabilities, such as inaccessibility to voters using wheelchairs.” To comply with ADA (1990) standards, polling centers need to be accessible to all voters. Suggestions to increase accessibility are drive-thru voting, wheelchair ramps, automatic doors, audio/video ballots, and having a volunteer that is trained on aiding those with disabilities at the polling booth. Areas for future research Future research needs to involve the voices of people with disabilities. They need to have representation in the conversation to determine their benefit. Of our seven articles reviewed, Agran et al. (2016) was the only article that focused on the voices of the clients. By not including their voice in research it alludes to this population not being able to provide their own opinions on topics. The second area of research should include the development of teaching strategies and resources for support personal and parents/guardians on how to provide unbiased instruction on candidate and political party standings. The research should include how to make abstract ideas concrete as well as how to develop and write civics goals for individuals with developmental disabilities. Future research could provide recommendations based on the type of disability as well. Future research should be conducted to determine if there was an increase or decrease in the voting gap between people with and without disabilities for the 2020 election. The research can include how many people voting in person or by mail and how many of those votes were cast by a person with a disability. Conclusion The authors examined the research question: What experiences do individuals with disabilities face when participating in the democratic process of voting? Education and divergent voice were determined to be overarching themes that describe the experiences as a whole. The implications of this meta-synthesis is that it guides support personnel and parents/guardians on how to provide assistance and support to adults with disabilities when voting, encouraging researchers to develop voting instruction focusing on conceptual content, and policymakers to review current legislation to make voting more accessible to all eligible voters. The importance of this topic is to ensure our democratic society integrates all people with special consideration on policies that directly affect people with disabilities. References Agran, M., & Hughes, C. (2013). “You can't Vote—You're mentally incompetent”: Denying democracy to people with severe disabilities. Research and Practice for Persons with Severe Disabilities, 38(1), 58-62. https://doi.org/10.2511/027494813807047006 Agran, M., MacLean, J., William E., & Kitchen, K. A. A. (2016). "My voice counts, too": Voting participation among individuals with intellectual disability. Intellectual and Developmental Disabilities, 54(4), 285-294. https://doi.org/10.1352/1934-9556-54.4.285 68

Agran, M., MacLean, W., & Katherine Anne Kitchen Andren. (2015). "I never thought about it": Teaching people with intellectual disability to vote. Education and Training in Autism and Developmental Disabilities, 50(4), 388-396. Agran, M., Root-Elledge, S., Moody, E., Ginn, H., & Estrada-Reynolds, V. (2020). Perceptions of service providers regarding the agency and capacity of people with intellectual disability to vote. Education and Training in Autism and Developmental Disabilities, 55(1), 3-16. American Association on Intellectual Disabilities and Developmental Disabilities (2020). Definition of Intellectual Disability. https://www.aaidd.org/intellectualdisability/definition Categories of Disability Under Part B of IDEA. (2019). https://www.parentcenterhub.org/categories/ Kingston, L. N. (2014). Political participation as a disability rights issue. Disability and Health Journal, 7(3), 259-261. https://doi.org/10.1016/j.dhjo.2014.04.007 Littell, J. H., Corcoran, J., & Pillai, V. K. (2008). Systematic reviews and meta-analysis. Oxford; New York; 4: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195326543.001.0001 Major, C. H., & Savin-Baden, M. (2010). An introduction to qualitative research synthesis: Managing the information explosion in social science research. London: Routledge, https://doi.org/10.4324/9780203497555 Matsubayashi, T., & Ueda, M. (2014). Disability and voting. Disability and Health Journal, 7(3), 285-291. https://doi.org/10.1016/j.dhjo.2014.03.001 Schur, L., & Kruse, D. (2018). Fact sheet: Disability and voter turnout in the 2018 elections [Fact Sheet]. Rutgers School of Management and Labor. Relations.https://smlr.rutgers.edu/sites/default/files/2018disabilityturnout.pdf Schur, L., & Adya, M. (2013). Sidelined or mainstreamed? Political participation and attitudes of people with disabilities in the United States. Social Science Quarterly, 94(3), 811-839. https://doi.org/10.1111/j.1540-6237.2012.00885.x Tracy, S. J. (2010). Qualitative quality: Eight “Big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851. https://doi.org/10.1177/1077800410383121 U.S. Department of Justice. (2014, September). The Americans with Disability Act and other federal laws protecting the rights of voters with disabilities. https://www.ada.gov/ada_voting/ada_voting_ta.htm About the Authors Alena Bates, M.Ed., is a Doctoral student in the Department of Educational Psychology at the University of North Texas. She is a Board-Certified Behavior Analyst and a behavior resource specialist for a public school district in the state of Texas. Before serving students with disabilities at the elementary level, she began her career by serving adults with an intellectual disability at a therapeutic respite camp in Austin, TX. Working at the camp, she became inspired by the population to advocate for their political rights. Holly Collinsworth, M.Ed., is a Doctoral student in the Department of Educational Psychology at the University of North Texas. She is a Board Certified Behavior Analyst, a Behavior Instructional Specialist for a public school district, and an advocate for children and adults with 69

disabilities and their families. Throughout the last 22 years, she has served individuals with disabilities and their families in a variety of roles starting as a peer mentor and volunteer, before becoming a special education educator, behavior specialist, parent trainer, consultant, and researcher. She lives in Plano, Texas with her neurodiverse family of four and plans to continue to advocate for education and accessibility to voting participation for all. Cara Speicher is a Masters student in the Department of Educational Psychology at the University of North Texas. She has a Bachelor of Arts in English Letters from Southern Methodist University and is a Board Certified Assistant Behavior Analysts. Cara is a Behavior Instructional Specialist for a public school district, and has worked with individuals with disabilities and their families for 25 years. She has served individuals with disabilities and their families in a variety of roles starting as a volunteer, before becoming a special education educator, parent trainer, behavior specialist, advocate and consultant. She lives in Plano, Texas with her husband, two children, cat, and dog. She has dedicated her life's work to advocating for education and accessibility for all.

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Familiar vs. Unfamiliar Stimuli in Multiple Stimulus Preference Assessments Samantha Didrichsen, MSEd Mary. E. McDonald, Ph.D., BCBA Hofstra University Abstract This study examined the effect of including familiar and unfamiliar stimuli in a series of multiple stimulus preference assessment with a child with an autism spectrum disorder. Results showed that certain pairings of familiar and similar unfamiliar stimuli had a high frequency of being selected per session, and a high average duration of engagement. The results suggest that including similar unfamiliar stimuli in multiple stimulus preference assessments may be key in expanding the interests of individuals with autism and finding more preferred items to use as reinforcers. Familiar vs. Unfamiliar Stimuli in Multiple Stimulus Preference Assessments Reinforcement is a critical factor in the development of more socially appropriate behaviors (Pace, Invancic, Edwards, Iwata & Page, 1985). In order to increase such adaptive behaviors for those with disabilities, establishing a selection of potential reinforcers is a must, and should be a continuous process. It is important to generate a large selection of reinforcers that meet the interests of the individual to help motivate them throughout the intervention process. One way to be able to find potential reinforcers to be used in these interventions is to conduct a preference assessment by exposing the individual to an array of stimuli and recording the duration or frequency with each stimulus (Pace, Invancic, Edwards, Iwata & Page, 1985). Reinforcers must have a wide variety to them and can come in different forms for the individual to sample, such as sensory items, edibles, and tactile objects (Fisher, Piazza, Bowman, Hagopian, Owens & Slevin, 1992). Another factor that should be taken into account when selecting potential reinforcers to be used in the preference assessment is by interviewing people associated with the individual to help discover their interests, such as parents, teachers, and caregivers (Fisher, Piazza, Bowman, Amari, 1996). In one study, researchers wanted to maximize the array of potential reinforcers in a preference assessment by evaluating the preference hierarchy results with a group of 31 children with autism (Kenzer & Bishop, 2011). Potential reinforcers that were selected for the preference assessment were from one of three categories: 1) stimuli reported by staff as highly preferred, 2) stimuli reported by staff as less preferred, 3) and stimuli with an unknown preference, or novel stimuli. Surveys were given to staff to identify high and low preference stimuli. Novel stimuli were identified as stimuli that weren’t listed on the answered surveys or appeared to be similar to stimuli that were given from the staff on the surveys. Results from this study showed that a large majority of the children selected novel stimuli, and new reinforcers from the novel and low 71

preferred survey results became authentic reinforcers for 27 of the 31 children. These results support that even novel stimuli must be included in preference assessments, or else practitioners are restricting potential reinforcers in preference assessments to already familiar stimuli. Kenzer and Bishop later on wanted to study the preference and reinforcing effects of familiar and unfamiliar stimuli with young children with ASD (Kenzer, Bishop, Wilke, & Tarbox, 2013). Researchers conducted preference interviews with staff and caregivers of young children with ASD to find three familiar stimuli that are preferred items to these children. From there, they evaluated and found three more items that were similar unfamiliar stimuli, which are items that have similar factors to the familiar preferred items. They conducted a paired-preference assessment and studied the reinforcing effects of the items. Results of this study showed that similar unfamiliar stimuli had high percentages of selection and had an arbitrary response during the reinforcement assessment. Therefore, the purpose of this study will be to evaluate the preference strength of familiar stimuli and similar unfamiliar stimuli. The study above will be replicated and will be done with slight variations. It will start by initially ranking three highly preferred familiar stimuli and identifying three similar unfamiliar stimuli. A multiple stimulus assessment will be used to identify highly preferred stimuli from these items with a student with ASD. Frequency and duration data will be collected to determine preferred stimuli. Method Participants The child in this study was one student from a school that uses the principles of ABA. The student's age ranged from 6-10 years old and had a diagnosis of autism. Setting The setting was a room within the school where the sessions took place. There was a table, chairs, and familiar and similar unfamiliar stimuli (i.e., items used during preference assessment) for the student for the sessions. Materials The materials included in this study were familiar and unfamiliar stimuli for the student that was determined by the results of a preference interview. There were six items in total, where three were familiar stimuli, and three were similar unfamiliar stimuli. For each familiar stimulus, there was a similar unfamiliar stimulus. The similar unfamiliar stimulus shared similar properties to its matched familiar one. The similar unfamiliar item included the same function (e.g., playing a board game), the same stimuli (e.g., a cartoon character), or the same sensory properties (e.g., auditory stimulation) as the familiar stimulus. Design An ABAB design was used to determine the frequency of selection of a familiar and similar unfamiliar stimulus. The A phase measured the frequency of the students choosing a familiar stimulus, while the B phase measured the frequency of the student choosing a similar unfamiliar novel stimulus.

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Description of Dependent Measures The response definition for this study was the child selecting either a familiar or similar unfamiliar stimulus when presented with a multiple stimulus preference assessment by either touching an object, pointing to an object, or verbally requesting an object. The child selected an item when presented with the SD of “pick one.” The child interacted with the item for no more than a minute. Data Collection Data for the preference assessment was collected by recording the frequency of the individual requesting for a desired stimulus, which was defined as the student verbally asking for or nonverbally requesting the stimuli. The data was summarized by generating a frequency chart to show how many times an item was requested within a fifteen-minute session. A single trial lasted no more than one minute, where a student selected an item, and the selection was tallied. Duration data was also collected to determine a measure of preference of the item. The data was summarized by calculating the average duration a child played with each stimulus item and then was presented as a percentage of engagement during each session. Procedure Familiar Stimulus Condition Prior to the multiple stimulus preference assessment, an interview was conducted with a staff member to determine preferred familiar stimuli for this student. With this information, similar unfamiliar stimuli were determined. The staff member was questioned to make sure that each stimulus has never been exposed to the student as a preferred item. Once these stimuli were determined, the child was exposed to each stimulus for 2 minutes. Unfamiliar Stimulus Condition Treatment Sessions A multiple stimulus preference assessment was conducted for both the A and B phases. During each trial of the preference assessment, three stimuli were presented, and the SD was “pick one.” A selection was given 60 seconds of time with the student. Each trial had a random ordering of the items when presented to the student when taken out of a bag. During this time, frequency and duration data was collected. One to six sessions were conducted daily, 3 days per week, depending on availability. Return to Familiar Stimuli A multiple stimulus preference assessment was conducted that replicated the A phase, where the child was given an array of familiar stimuli to select from. Frequency and duration data were collected during this phase. Return to Unfamiliar Stimuli A multiple stimulus preference assessment was conducted that replicated the B phase, where the child was given an array of similar unfamiliar stimuli to select from. Frequency and duration data were collected during this phase.

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Interobserver Agreement (IOA) Interobserver agreement data was collected during every preference assessment session. During preference assessments, an agreement was scored if both observers recorded the same selection on a trial. Interobserver agreement data was calculated on independent variables, such as placing the items randomly, providing the requested item, ending the trial on time, and bringing the correct items in for that specific phase. Interobserver agreement data was calculated on dependent variable such as the student selecting an item by either touching an item, pointing to an item, or verbally requesting for an item. Interobserver agreement was calculated by dividing the number of agreements by the total number of trials and multiplying by 100 to obtain a percent agreement. Results Results of the multiple stimulus preference assessments are displayed in Figure 1 and Figure 2. During the first round of Phase A, the participant had a high frequency of selecting both the hard Playdough and the stretchy frog, as can be seen in Figure 1.

Figure 1. The frequency was calculated by determining the amount of times an item was requested out of 15 trials for 1 session.

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The final data point showed that he selected both stimuli 7 times during the final trial of the first round of Phase A, with the average duration of engagement with both stimuli matching at 46% for that session, which can be seen in Figure 2.

Figure 2. The duration of engagement with stimuli was calculated by determining the average duration of engagement for an item during a 15 minute session. This was nearly replicated in the first round of Phase B, where the student engaged with the similar unfamiliar stimuli that were paired with the hard Playdough and the stretchy frog. On the final trial for the first round of Phase B, the student selected the soft Playdough 7 times and the stretchy putty 6 times. The average duration of engagement for the soft Playdough during this time period was also at 46%, and the average duration of engagement for the stretchy putty was at 35%. The second round of both Phase A and Phase B showed similar data for these two pairings of familiar and similar unfamiliar stimuli. Discussion Results showed that certain pairings of the familiar and similar unfamiliar stimuli had a high frequency of being selected per session, and a high average duration of engagement. The student was highly engaged in the different types of Playdough and the stretchy materials. As time went 75

on, the frequency of selecting the shiny pinwheel and shiny necklace decreased, which also decreased the average duration of engagement for these stimuli. This may suggest that for this student, stimuli that can be molded and stretched may be more preferred than shiny stimuli. Because of this, the results suggest that including similar unfamiliar stimuli in multiple stimulus preference assessments may be key in expanding the interests of individuals with autism and finding more preferred items to use are reinforcers. However, there were limitations to this study. There was a time constraint that cut the amount of sessions short for the second round of Phase A and Phase B. Instead of going for 5 sessions for these phases again, it had to be cut down to 3 to adjust for time. There was also the possibility that during the trials, the similar unfamiliar stimuli may have actually become familiar to the student through exposure. Future research should expand on the time period for each phase so as to collect more data to analyze for relationships in stimuli factors. References Fisher, W. W., Piazza, C. C., & Bowman, L. G. (1996, July). Integrating caregiver report with a systematic choice assessment to enhance reinforcer identification. American Journal on Mental Retardation, 101(1), 15-25. Fisher, W., Piazza, C. C., Bowman, L. G., Hagopian, L. P., Owens, J. C., & Slevin, I. (1992). A comparison of two approaches for identifying reinforcers for persons with severe and profound disabilities. Journal of Applied Behavior Analysis, 25(2), 491-498. Kenzer, A. L., Bishop, M. R., Wilke, A. E., & Tarbox, J. R. (2013). Including unfamiliar stimuli in preference assessments for young children with autism. Journal of Applied Behavior Analysis, 46(3), 689-694. Kenzer, A. L., & Bishop, M. R. (2011). Evaluating preference for familiar and novel stimuli across a large group of children with autism. Research in Autism Spectrum Disorders, 5, 819-825. Lim, L., Browder, D. M., & Bambara, L. (2001). Effects of sampling opportunities on preference development for adults with severe disabilities. Division on Mental Retardation and Developmental Disabilities, 36(2), 188-195. Mason, S. A., McGee, G. G., Farmer-Dougan, V., & Risley, T. R. (1989). A practical strategy for ongoing reinforcer assessment.Journal of Applied Behavior Analysis, 22(2), 171-179. Pace, G. M., Ivancic, M. T., Edwards, G. L., Iwata, B. A., & Page, T. J. (1985). Assessment of stimulus preference and reinforcer value with profoundly retarded individuals. Journal of Applied Behavior Analysis, 18(3), 249-255. About the Authors Samantha Didrichsen received her BSEd in Early Childhood Education at the State University of New York at Fredonia. As a graduate of the HECIS program, she also received her MSEd in Special Education Early Childhood Intervention at Hofstra University. Samantha lives in Buffalo, NY and is currently a SEIT provider through the Buffalo Hearing & Speech Center, giving support and instruction to infants, toddlers and preschoolers with delays and disabilities. Mary E. McDonald, Ph.D, BCBA-D, LBA: Dr. Mary E. McDonald is a Professor in the Department of Specialized Programs in Education at Hofstra University in Hempstead, NY. She 76

is currently the Program Director for the Advanced Certificate Programs including the Advanced Certificate in Applied Behavior Analysis Program. She is a Board Certified Behavior Analystdoctoral level and a licensed behavior analyst in the states of NY and CT. Dr. McDonald has worked in the field for over 30 years and currently directs programs for students with autism at Eden II’s Genesis Programs. She has published a book on including students with ASD as well as chapters on technology and evidence-based interventions. She has published peer-reviewed and popular articles on topics such as: self-management, social reciprocity, PECS, scripts and semantic mapping, creativity and Universal Design for Learning.

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Community Factors and High School Services for Diploma-Track Students on the Autism Spectrum Jade LaRochelle, OTD, OTR/L* Elizabeth G. S. Munsell, Ph.D., MS, OTR/L* Elizabeth K. Schmidt, Ph.D., OTR/L Gael Orsmond, Ph.D. Wendy Coster, Ph.D., OTR/L, FAOTA Boston University *Authors contributed equally as first authors. Abstract Diploma-track youth on the autism spectrum often exhibit poor post-school outcomes. These poor outcomes may be influenced by schools’ resources for providing transition supports to these students. The authors analyzed associations between school personnel’s reports of services provided to students on the autism spectrum and community socioeconomics (median household income; MHI). The authors further examined how average time spent in general education and the number of diploma-track students moderated this relationship. MHI had significant, positive associations with the number of supports provided and negative associations with the number of referrals. The number of supports provided were moderated by time spent in general education and number of diploma-track students. Findings indicated differences in the number of transition supports provided based on MHI. Schools in lower MHI communities face the challenge of devising low-cost alternatives to providing comprehensive transition supports. Keywords: Autism spectrum disorder, transition services, high schools, postsecondary Community Factors and High School Services for Diploma-Track Students on the Autism Spectrum Autism spectrum disorder (ASD) is a developmental disability characterized by differences in social communication and restricted, repetitive behaviors (American Psychiatric Association, 2013). Youth on the autism spectrum tend to have poorer postsecondary outcomes than youth without ASD in the areas of employment, postsecondary education, independent living, and social participation (Roux et al., 2015). One third of youth on the autism spectrum were reported to be “disconnected,” meaning not participating in employment or education in their early 20’s, compared to the United States national average of 7.6% for all youth (Ross & Svajlenka, 2016; Roux et al., 2015). Poor post-school outcomes are not limited to youth with the most severe symptoms or co-occurring intellectual disability, but are seen among youth with intellectual ability within the average range as well. Few studies have focused specifically on outcomes and transition services for youth on the spectrum who meet the academic requirements to receive a high school diploma, or “diplomatrack students”. One study on participation in postsecondary activities of youth on the autism 78

spectrum found that those youth with average to above average intelligence were three times more likely than youth with intellectual disabilities not to participate in any daytime activities post-graduation from high school (Taylor & Seltzer, 2011). Additionally, in a longitudinal study of the same sample, one third of youth on the autism spectrum without intellectual disabilities had never engaged in employment or post-secondary education during the four and a half-year study period (Taylor et al., 2015). Taylor et al. (2015) did not report what percentage of their sample graduated with a regular diploma, but it is reasonable to assume that the sample included a substantial number who did. This evidence indicates that many diploma-track students on the autism spectrum have poor transition outcomes, despite their intellectual abilities. Transition supports and services have been linked to postsecondary outcomes for students with disabilities (Haber et al., 2016). For diploma track-students on the autism spectrum these pregraduation transition services are especially important as these students may not have access to the same level of comprehensive and individualized services provided in schools after graduation. Best practice recommendations for transition services exist (Kohler et al., 2016); however, the available literature suggests that there are disparities in service provision and outcomes for students on the autism spectrum (Eilenberg et al., 2019). Variations in community resources provide one explanation for these disparities. A systematic review of disparities for youth on the autism spectrum found that low income and minority youth were less likely to participate in transition planning meetings, obtain competitive employment, participate in postsecondary education, and live independently (Eilenberg et al., 2019). While policies have been put in place in an attempt to prevent these disparities (i.e., Every Student Succeeds Act, 2015; ESSA), civil rights activists and researchers alike are questioning the effects of these policies in promoting equity (Egalite et al., 2017). This is particularly relevant in Massachusetts, which has a decentralized education system, meaning that local education officials are able to make many decisions for their school districts instead of relying on state regulations (Snow & Williamson, 2015). There are concerns that decentralization can contribute to inequity if school leaders do not take the appropriate steps to analyze and disseminate education data and appropriately address disparities (Egalite et al., 2017). The aforementioned studies on disparities related to community factors provide a rationale for exploring whether existing policies achieve the goal of preventing demographic and community factors from impacting services. The purpose of this study is to investigate the relationships between community socioeconomics and high school services provided to diploma-track students on the autism spectrum. The existing literature on this topic focuses on youth of all ages along the entire autism spectrum, both with and without intellectual disability, and has typically not specified whether service availability refers to services received in schools. Further, there are no studies that investigate this relationship specifically for diploma-track students on the autism spectrum, whose schoolbased transition services may be unique due to their relative academic strengths, which result in them spending the majority of their educational time in general education (Orsmond et al., 2020). This study adds to this literature as it focuses on the subpopulation of diploma-track students preparing for transition to adulthood. In addition, studies regarding disparities in services have typically utilized caregiver reports (Pickard & Ingersoll, 2016). While these studies provide important information on caregiver perspectives, the reported services are not specific to the school setting, and caregivers may have different perspectives on service provision than those of 79

school personnel. Due to the number of students school personnel see and their roles within the school, they likely have a better understanding of a school or district’s programmatic approach to serving these students. In the present study the authors surveyed school personnel with knowledge of services provided to diploma-track students on the autism spectrum. Based on the existing literature on disparities in service availability and outcomes related to socioeconomic status, the authors focused on the relationship between median household income (MHI) of a community and transition services provided to these students. The research questions were: (1) Is the socioeconomic background (MHI) of a school’s community associated with the number of transition services and referrals provided to diploma-track high school students on the autism spectrum? (2) Does the average time spent in general education or number of diploma track students moderate the relation between MHI and supports and referrals provided? Exploring the relationships between socioeconomics and service provision for this population may increase understanding of where service disparities are occurring, which can support school districts to narrow these gaps to promote more consistent, effective practices. Method Participants The researchers analyzed data from an online survey of high school personnel in Massachusetts. Inclusion criteria included: 18 years of age or older; working in a school or school district in Massachusetts; and having knowledge of the supports and services provided to students in their high school or district who have autism and plan to graduate with a regular diploma, rather than a certificate, modified diploma, or other document. For the original study, participants were recruited from both public and private high schools and school districts; however, only personnel from public, non-charter schools were included in the present analysis. Participants from nonpublic and charter schools were excluded due to the study’s emphasis on school socioeconomic factors indicated by MHI. Resources in charter and private schools are likely less influenced by the community’s MHI due to different funding sources. Materials and Procedures Survey content. The survey contained 64 items with 60 multiple choice items and four short answer items, taking approximately 45-60 minutes for completion. The authors developed questions based on focus group interviews conducted with school personnel and a literature review of best practices in transition planning and common high school supports (Orsmond et al., 2020). The online survey included questions related to participant characteristics and role in the school or district, school or district characteristics, general information on diploma-track students on the autism spectrum, and services and supports provided to these students. Further detail of the processes of survey development and content can be found in Orsmond et al. (2020). Survey administration. Participants were recruited through emails to various school personnel in districts and schools across Massachusetts. School personnel emails were collected via school 80

and district websites, as well as personal contacts. Priority for emailing was based on the likelihood of interaction of the school personnel with this student population and adequate knowledge about the services and supports provided to these students. The researchers first contacted special education directors, special education team chairs and administrators, transition coordinators, and autism specialists. Second, the researchers contacted school psychologists, special education teachers, occupational therapists, physical therapists, guidance counselors and general special education staff. The email contacts provided school personnel with information about the study and inclusion criteria. Further details of the recruitment process can be found in Orsmond et al. (2020). Completely Automated Public Turing Test Computers and Humans Apart (CAPTCHA) verification embedded in the online survey identified the participants as human participants. Each participant had 72 hours to complete the survey, and the responses of the participants were saved anonymously. In a separate form, participants could select to provide contact information in order to be entered into a drawing to win an iPad. The survey was active for ten months starting in September 2017. During those ten months, at least one school personnel was contacted from approximately 480 schools. Zip code data were used throughout this process to ensure a representative sample. Personnel from school districts that did not have a respondent were sent up to three reminder emails to increase representation across Massachusetts. The survey was opened a total of 231 times, with 107 participants completing the survey. Seventy public school personnel were identified from the larger sample for the purposes of this analysis. Compliance with ethical standards. The researchers obtained informed consent from all participants included in this study. Recruitment and study procedures were approved by the Boston University Internal Review Board. Data Analysis Dependent variables. Twenty-eight transition-related supports or services from the online survey were included in this analysis. Participants were asked whether each listed service was regularly provided, occasionally provided, never provided, or provided via referral to an outside agency. These choices were mutually exclusive. In order to understand a school’s regular programmatic approach to service provision, which may be more likely to be influenced by school resources, the researchers only included those services which were “regularly provided,” or “provided via referral” rather than “occasionally provided,” as services provided occasionally may be less programmatic. The primary dependent variable was the total number of supports and services and referrals provided. Each “regularly provided” service/referral was counted as one and summed to identify the total number of supports and services and referrals provided in the school or district to diploma-track students on the autism spectrum. Using the same approach, the researchers created three sub-categories for the number of employment supports or referrals, independent living supports or referrals, and postsecondary education supports or referrals (see Table 1 for services included in each category). Table 1 Service Types Included Within Composite Variables Composite variable

Service types from survey 81

Employment supports/referrals

Interview skills, Resume writing, Career exploration, Job coaching, Job shadowing, Setting up job accommodations

Postsecondary education supports/referrals

Study skills, Organizational skills, Visits to local colleges prior to enrollment, Concurrent enrollment in high school and postsecondary classes, Social skills instruction for future postsecondary education settings, Establishing peer mentoring relationships for post-secondary education settings, Instruction for navigating ADA accommodations in postsecondary settings

Independent living supports/referrals

Self-care, Independent living, Financial literacy/money management, Travel in the community, Safety in the community

Overall supports/referrals

Employment supports, Postsecondary education supports, Independent living supports AND Behavioral interventions, Leisure exploration and/or skills development, Civic engagement (e.g. volunteering, participating in community organization, etc.), Driver’s education, Mental health symptom management, Sexual health, Assistive technology, Social skills, Disability disclosure and/or advocacy, Self-determination/problem solving/goal setting

Cronbach’s alpha indicated acceptable to strong internal consistency for all supports and services categories: overall supports = .90, postsecondary education = .70, independent living = .89, and employment = .87. All four summary variables met the assumptions for normal distribution and thus were treated as continuous variables in our data analysis. In general, schools provided very few referrals so these summary variables were not normally distributed and had low internal consistency (Cronbach’s alpha for overall supports = .84, postsecondary education = .29, independent living = .81, and employment = .75). Non-parametric statistical analyses were used for the referral variables. Independent variables. Median household income (MHI) of the school’s community was assigned based on the participant reported zip code of their school or district. All but nine participants reported unique zip codes. The researchers only included repeat zip codes if there were multiple high schools within the given zip code. In all other cases in which the participants reported on the same school, the responses were averaged. Each zip code was then assigned a MHI using 2013-2017 US Census data (United States Census Bureau, 2018). Two additional independent variables were used in the analyses to explore potential moderators of the relationship between MHI and services: Number of diploma-track students and time spent in general education. These factors likely vary by school and may be influenced by programmatic approaches and affect supports received. First, we included the number of diploma-track students to explore whether having more of these students was associated with service frequency, which may be related to resource availability, as having more students would likely require more resources. Participants reported on the number of diploma-track students on the autism spectrum who graduated in the last year (i.e. 0-5, 6-10, 11-15, 16-20, 21+ students). Approximately 50% of respondents selected the 0-5 category and 50% selected 6 or more. Therefore, we used a median split and dichotomized the variable into 0-5 students and 6 or more 82

students. Second, we explored any moderating effects of the average percentage of time that diploma-track students spent in general education, as the most common setting of service provision may relate to services provided. Participants also reported the average amount of time that diploma-track students on the autism spectrum spent in general education. While the response options were categorical (i.e., 0-30%, 31-60%, 61-90%, 91-100%), this variable fit skew and kurtosis criteria for normal distribution and was treated as continuous for analysis. Data analysis. Data were analyzed using Statistical Package for Social Sciences (SPSS), version 26. Descriptive analyses of mean, median, range, or frequencies were calculated to describe demographic characteristics, and number of supports and referrals provided. Associations between MHI and the summary support and between MHI and referral variables were tested using parametric and non-parametric correlations, respectively. Linear stepwise regressions were used to test association between MHI and number of supports provided, as well as moderation of this association by number of diploma-track students and time spent in general education. For all analyses, two tailed p values are reported and an alpha value of .05 is used to indicate statistical significance unless otherwise noted. Results Demographic Characteristics of Participants and Schools The majority of the participants were female (80%), mean age 45.6 years (range 27-66), with an average of 6.8 years working at their current position and 12.5 years working with diploma-track students on the autism spectrum. Participants held a variety of positions in their schools or school districts, but were primarily special education coordinators (23%), special education teachers (21%), and directors of special education (19%). Participants were from urban (n = 26), suburban (n = 41), and rural (n=3) school districts. Schools and districts included in this sample were predominantly white and non-Hispanic. Table 2 describes the school characteristics of the sample. Table 2 School characteristics School characteristic Community type Suburban Urban Rural Median household income Mean percent of student population in each racial category White Black Asian Two or more races Mean percent of student population in each ethnic category Non-Hispanic Hispanic Number of students in district 83

%* (n) or Mean (SD) 58.6% (41) 37.1% (26) 4.3% (3) $91,414.84 ($36,120.15) 67.49 (32.20) 5.79 (8.88) 5.44 (8.65) 5.09 (10.83) 57.74 (42.10) 14.23 (20.39)

Fewer than 300 7.1% (5) 300-999 24.3% (17) 1,000-1,999 32.9% (23) 2,000-3,999 12.9% (9) 4,000-6,999 4.3% (3) 7,000-10,000 2.9% (2) Over 10,000 8.6% (6) Number of students with autism that graduated with regular diploma in past year 0-5 55.7% (39) 6+ 41.4% (29) Time diploma-track students spend in general education 0-30% 1.4% (1) 31-60% 10% (7) 61-90% 55.7% (39) 91-100% 20% (14) *Not all percentages add to 100 due to missing data. Number of Supports and Referrals Provided The average number of transition supports provided was 11.7 (SD = 6.89, range= 0-25, maximum possible number of supports = 29). Within the sub-categories of supports, on average, schools provided 2.56 employment supports (SD = 2.16, range = 0-6, maximum possible number of supports = 6), 2.90 postsecondary education supports (SD = 1.73, range = 0-7, maximum possible number of supports = 7), and 1.84 independent living supports (SD = 2.00, range = 0-5, maximum possible number of supports = 5). Schools reported providing fewer referrals than supports: the average number of referrals overall was 2.44 (SD = 3.21, range= 0-13). The average number of referrals for employment was 0.75 (SD = 1.29, range 0-6), postsecondary education was 0.36 (SD = .622, range 0-3), and independent living was 0.46 (SD = 1.09, range= 0-5). Associations between MHI and Number of Supports or Referrals Provided MHI had a positive, significant association with three of the dependent variables: number of regularly provided overall supports (r (59) = .42, p = .001), postsecondary education supports (r (59) = .28, p = .030), and independent living supports (r (59) = .43, p = .001). However, MHI had a negative, significant association with all referral categories: overall referrals (rs (59) = -.54, p < .001), postsecondary education referrals (rs (59) = -.32, p = .012), employment referrals (rs (59) = -.48, p < .001), and independent living referrals (rs (59) = -.29, p = .022). Schools with higher MHI provided more supports and fewer referrals, whereas schools with lower MHI tended to provide fewer supports and more referrals. In addition to testing the association between the four categories of supports and referrals, the researchers also completed exploratory analyses of the associations between individual supports and referrals with MHI. Four types of supports reached statistical significance at an adjusted alpha level (p < .002) with small to moderate, positive associations with MHI: independent living (r (59) = .40, p = .002), community travel (r (59) = .38, p = .002), community safety (r (59) = .42, p = .001), and ADA accommodations for postsecondary education (r (59) = .46, p = 84

.00). There were also small to moderate, negative associations with MHI and individual types of referrals (rs (59) = -.26, -.37), but none of these reached significance at the adjusted alpha level. Number of Diploma-track Students on the Autism Spectrum The number of students on a diploma-track at the school or school district was not significantly associated with MHI (F (1,66) = .03, p = .865). However, the number of diploma-track students moderated the association between MHI and number of overall supports (p = .016; see Table 3). Table 3 Moderators of Association between Median Household Income and Services Provided Std. Beta T p R2 Number of autistic diploma-track students Overall supports .227 MHI .357 3.02 .004 Number of students .113 0.98 .333 MHI x Number of students .292 2.48 .016 Δ R2= .082 Time spent in general education Overall supports MHI Time spent in general ed. MHI x Time spent in general ed.

.443 .116 -.300

.262 3.52 .001 .941 .351 -2.52 .015 Δ R2= .085

Employment supports MHI Time spent in general ed. MHI x Time spent in general ed.

.228 .217 -.301

.176 1.71 .093 1.67 .101 -2.38 .021 Δ R2= .085

Postsecondary education supports MHI Time spent in general ed. MHI x Time spent in general ed.

.296 .272 -.360

.273 2.37 .021 2.23 .030 -3.04 .004 Δ R2= .112

Schools with six or more students in higher MHI zip codes tended to provide more supports than schools with 0-5 students (Figure 1).

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Figure 1. Moderation of Number of Diploma-Track Students Percent of Time Spent in General Education The average amount of time that diploma-track students on the autism spectrum spent in general education was significantly associated with MHI (r = .31, p = .016) with students spending more time in a general education setting in schools with higher MHI. Furthermore, the amount of time spent in general education moderated the association between MHI and number of overall supports (p = .015), employment supports (p = .021), and postsecondary education supports (p = .004), but not independent living supports (p = .209) (Table 3). In general, schools with lower MHI who reported more time spent in general education also reported a higher number of supports provided compared to those who reported less time in general education. (Figure 2).

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Figure 2. Moderation of Time Spent in General Education Discussion The purpose of this study was to investigate the relationship between community socioeconomics and supports provided to diploma-track students on the autism spectrum. The results of this study support previous work and add a focus on school-based transition supports for diploma-track students on the autism spectrum. The number of supports provided to diplomatrack students on the autism spectrum was significantly associated with MHI. These findings revealed some moderating factors for this association. While high MHI schools offered more supports overall, schools with higher MHI may offer more supports when they have more diploma-track students in the school. The researchers also saw a moderation effect with time spent in general education on MHI. In general, schools in neighborhoods with higher MHI reported providing more supports regardless of how much time diploma-track students spent in general education. However, schools in lower MHI may offer more supports in their general education than their special education curriculum. These findings provide some insight into additional factors that may influence the relationship of MHI and transition support provision, suggesting schools in higher MHI are better equipped to support greater numbers of students. These findings may be due in part to resource availability and possibly parental factors (e.g., parent advocacy). For example, in Massachusetts, a state with decentralized education systems, local property taxes influence school funding. Although states allocate a certain amount of funds to each school dependent on need, certain local districts can choose to allocate a greater percent of their taxes towards the local district, meaning schools that are in higher income neighborhoods may have greater funds despite a lesser need compared to other districts (Massachusetts Budget, 2010). Additionally, research demonstrates that parents with greater socioeconomic status and higher education may have the means and ability to 87

advocate for greater supports (Newman, 2005). Further, our findings demonstrate schools in lower MHI may offer more supports in their general education curriculum than their special education curriculum, as evidenced by the moderation effects of the number of diploma-track students and time spent in general education. Previous research revealed that educators at the secondary level believed that students with special education needs should be in general education settings, but that they would need supportive assistance in order to be successful in this environment (Idol, 2006). Secondary educators describe using a modified curriculum and collaborative instruction methods to ensure that the needs of their students are met (Idol, 2006), which may influence the overall supports and services provided to students in our study. The findings indicating discrepancies in the number of supports in relation to MHI are particularly significant in light of the federal guidelines that require schools to address disparities among students in low-income families as compared to those in higher-income families (Egalite et al., 2017). ESSA (2015) serves as a mechanism to support state and local education officials in improving educational opportunities by providing federal funds, offering some guidance for monitoring outcomes and addressing disparities to better support low-performing schools or children from low-income families (Egalite et al., 2017). However, the associations between MHI and the number of supports provided for overall transition, postsecondary education, and independent living supports provide further evidence that disparities exist among lower income schools. These disparities may be impacting youth on the autism spectrum and should be addressed in accordance with ESSA. The findings did reveal some possible strategies schools with lower income and fewer resources may be using to combat these challenges. First, school districts in neighborhoods with lower MHI were more likely to refer to outside agencies for additional supports. However, the number of referrals provided to students in lower MHI schools did not compensate for the number of services received at school by students in schools within higher MHI communities. Furthermore, the study did not determine if students in the schools providing referrals pursued services outside of school. Research suggests that disparities also exist regarding service access following referrals (Zuckerman et al., 2014). Given these disparities schools in low MHI communities face the challenge of devising low-cost alternatives to providing comprehensive transition preparation for diploma-track students on the autism spectrum. Lower MHI schools may consider providing transition supports that are evidence-based but do not require additional resources. Schools may use existing personnel, proximity to existing community resources, and free curricula to provide services (National Technical Assistance Center on Transition, 2018). Limitations and Future Directions The sample in the current analysis is limited in size and focus on schools in Massachusetts. Specifically, the researchers only have data from 70 school personnel from public schools in Massachusetts, which is only 4% of the public schools within the state. This state is also unique in that education is decentralized within the state, meaning local school administrators have greater autonomy in allocating resources. This may limit generalizability to other states, particularly those in states with centralized education systems. Additionally, the mean median household income of the communities in this study was substantially higher than the 2017 national median household income ($61,372) which may decrease generalizability to other states 88

with median household income lower than the national average (Fontenot et al., 2018). However, local and national studies addressing variations in school-based transition service provision are currently lacking; therefore, this study provides an initial understanding of these relationships within one state in which local education agencies are able to make many decisions about service delivery independently, providing variability in the types of educational programs sampled in this study. Another limitation was the method of measuring service provision. The researchers asked school personnel to identify if these services were regularly provided, but the survey did not obtain information from school personnel on what they considered “regularly provided,” leaving room for interpretation. Additionally, while the findings provide an understanding of the quantity of services provided to these students, the survey questions did not allow for exploration of quality. Given these limitations, this work needs to be replicated with larger samples across the United States. Additionally, the researchers found moderate associations between MHI and frequency of transition supports. Future work could employ a more specific measurement of school resources that could reveal stronger correlations with service provision or include other factors (i.e., parental engagement/advocacy, parent education, family public assistance, and race/ethnicity) in analyses that may explain additional variability in number of services provided (Eilenberg et al., 2019; Dallman et al., 2020). Additionally, MHI is only one way to understand potential school resources for providing services to these students. Future studies could examine availability of school resources through other variables, such as number of school personnel working with this population and amount of school budget allocated to students who receive special education or transition services. And finally, continued research is needed to understand the effects of programmatic approaches that utilize implementation science to assist educators and administrators in implementing evidence-based interventions for youth on the autism spectrum in feasible ways (Anderson et al., 2020). References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596 Anderson, C., Iovannone, R., Smith, T., Levato, L., Martin, R., Cavanaugh, B., Hochheimer, S., Wang, H. & Iadarola, S. (2020). Thinking small to big: Modular approach for autism programming in schools (MAAPS). Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-020-04532-1 Dallman, A. R., Artis, J., Watson, L., & Wright, S. (2020). Systematic review of disparities and differences in the access and use of allied health services amongst children with autism spectrum disorders. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-020-04608-y Egalite, A. J., Fusarelli, L. D., & Fusarelli, B. C. (2017). Will decentralization affect educational inequity? The Every Student Succeeds Act. Educational Administration Quarterly, 53(5), 757-781. https://doi.org/10.1177/0013161X17735869 Eilenberg, J. S., Paff, M., Harrison, A. J., & Long, K. A. (2019). Disparities based on race, ethnicity, and socioeconomic status over the transition to adulthood among adolescents and young adults on the autism spectrum: A systematic review. Current Psychiatry Reports, 21(32), 1-16. https://doi.org/10.1007/s11920-019-1016-1 Every Student Succeeds Act, 20 U.S.C. § 6301 (2015). https://www.congress.gov/bill/114thcongress/senate-bill/1177 89

Fontenot, K., Semega, J., & Kollar, M. (2018). Income and poverty in the United States: 2017. U.S. Census Bureau. https://www.census.gov/content/dam/Census/library/publications/2018/demo/p60263.pdf Haber, M., Mazzotti, V., Mustian, A., Rowe, D., Bartholomew, A., Test, D., & Fowler, C. (2016). What works, when, and for whom, and with whom: A meta analytic review of predictors of postsecondary success for students with disabilities. Review of Educational Research, 86(1), 123-162. https://doi.org/10.3102/0034654315583135 Idol, L. (2006). Toward inclusion of special education students in general education: A program evaluation of eight schools. Remedial and Special Education, 27(2), 77-94. Kohler, P. D., Gothberg, J. E., Fowler, C., and Coyle, J. (2016). Taxonomy for transition programming 2.0: A model for planning, organizing, and evaluating transition education, services, and programs. Western Michigan University. https://www.transitionta.org/system/files/resourcetrees/Taxonomy_for_Transition_Progra mming_v2_508_.pdf?file=1&type=node&id=1727&force= Massachusetts Budget. (2010). Demystifying the chapter 70 formula: How the Massachusetts education funding system works. https://massbudget.org/reports/pdf/Facts_10_22_10.pdf Newman, L. (2005). Family involvement in the educational development of youth with disabilities: A special topic report from the National Longitudinal Transition Study-2. United States Department of Education. https://files.eric.ed.gov/fulltext/ED489979.pdf National Technical Assistance Center on Transition (2018). Using SDLMI to teach goal setting and problem-solving. https://www.transitionta.org/system/files/resourcetrees/Using SDLMI to Teach Goal Setting and Problem-Solving_1.pdf Orsmond, G.I., Munsell, E.G.S., & Coster, W. (2020). The status of service and support provision for diploma-track high school students on the autism spectrum. Journal of Special Education Leadership, 33(2), 90-105. Pickard, K. E. & Ingersoll, B. R. (2016). Quality versus quantity: The role of socioeconomic status on parent-reported service knowledge, service use, unmet service needs, and barriers to service use. Autism, 20(1), 106-115. doi: 10.1177/1362361315569745 Ross, M. & Svajlenka, N. P. (2016). Employment and disconnection among teens and young adults: The role of place, race, and education. The Brookings Institution. https://www.brookings.edu/research/employment-anddisconnection-among-teens-and-young-adults-the-role-of-place-race-and-education/ Roux, Anne M., Shattuck, Paul T., Rast, Jessica E., Rava, Julianna A., and Anderson, Kristy, A. (2015) National autism indicators report: Transition into young adulthood. Drexel University. https://drexel.edu/autismoutcomes/publications-andreports/publications/National-Autism-Indicators-Report-Transition-to-Adulthood/ Snow, D., & Williamson, A. (2015). Accountability and micromanagement: Decentralized budgeting in Massachusetts school districts. Public Administration Quarterly, 39(2), 220258. https://www.jstor.org/stable/24772854. Taylor, J. L., Henninger, N. A., & Mailick, M. R. (2015). Longitudinal patterns of employment and postsecondary education for adults with autism and average range IQ. Autism, 19(7), 785-793. https://doi.org/10.1177/1362361315585643 Taylor, J. L. & Seltzer, M. M. (2011). Employment and post-secondary educational activities for young adults with autism spectrum disorders during the transition to adulthood. Journal of Autism and Developmental Disorders, 41, 566-574. https://doi.org/10.1007/s10803-010-1070-3 90

United States Census Bureau. (2018). 2013-2017 ACS 5-year estimates [Data file]. Retrieved from https://www.census.gov/programs-surveys/acs/technical-documentation/table-and geography-changes/2017/5-year.html Zuckerman, K. E., Mattox, K. M., Sinche, B. K., Blaschke, G. S., & Bethell, C. (2014). Racial, ethnic, and language disparities in early childhood developmental/behavioral evaluations: A narrative review. Clinical Pediatrics, 53(7), 619-631. https://doi.org/10.1177/0009922813501378 About the Authors Jade LaRochelle, OTD received her Doctorate of Occupational Therapy from Boston University. Throughout her graduate education and research, she focused on transition and future planning for individuals on the autism spectrum. She is now a practicing occupational therapist with experience working in inpatient psychiatric settings and with youth and adults with intellectual and developmental disabilities (IDD). Elizabeth G. S. Munsell, Ph.D., MS, OTR/L is a post-doctoral fellow at the Center for Rehabilitation Outcomes Research at Shirley Ryan AbilityLab and the Center for Education in Health Sciences at Feinberg School of Medicine of Northwestern University. She received her masters and PhD at Boston University Sargent College of Health and Rehabilitation Sciences. Her clinical occupational therapy experience includes public school-based therapy, community life skills programs, and acute rehabilitation. Her research aims to enhance the function and community participation of young adults with disabilities, with a particular focus on understanding the needs of youth with neurodevelopmental disabilities entering adulthood and how to assess meaningful transition outcomes to inform clinical intervention. Elizabeth K. Schmidt, Ph.D., OTR/L is an Assistant Professor and the Director of Research for the Department of Occupational Therapy at Lincoln Memorial University in Knoxville, Tennessee. She received her masters and PhD at the Ohio State University’s Health and Rehabilitation Sciences Program and completed her postdoctoral fellowship at Boston University Sargent College of Health and Rehabilitation Sciences. Her clinical occupational therapy experience includes public and charter school-based therapy and outpatient pediatrics. She is currently funded by the National Institutes of Disability, Independent Living and Rehabilitation Research to conduct qualitative research with LGBTQIA+ autistic adults to better understand the supports and barriers to community participation using a participatory research approach. Her research is primarily focused on supporting participation and accessibility of sexuality education for individuals with neurodevelopmental disabilities. Gael Orsmond, Ph.D., received her doctorate in Clinical and Developmental Psychology from the University of Illinois at Chicago, followed by post-doctoral training at the Waisman Center at the University of Wisconsin-Madison. Since 2000, she has been at Boston University, where she is now Professor of Occupational Therapy and Associate Dean of Academic Affairs in the College of Health & Rehabilitation Sciences: Sargent College. She has devoted her career to understanding how the family, social, community, and school contexts are crucial to the development and well-being of adolescents and adults on the autism spectrum. Her research has explored social and community participation of autistic adolescents and adults, sibling and family relationships, and understanding factors that optimize a positive transition to adulthood 91

for this population. Her research has been funded by the National Institutes of Health and the U.S. Department of Education’s Institute of Education Sciences. Wendy Coster, Ph.D., OTR/L, FAOTA is Professor Emeritus of Occupational Therapy in the College of Health & Rehabilitation Sciences: Sargent College, Boston University. Her initial professional education, at Boston University, was in occupational therapy and she spent six years providing services to children and youth with emotional and cognitive disabilities in schools and community agencies. She received her doctorate in Psychology from Harvard University and began her career at Boston University in 1986. Over the course of her career she directed and engaged in multiple projects to develop better assessments of home, school, and community functioning for children, youth, and adults that recognized their strengths and the important supports that enabled their successful participation. Among the instruments she developed are the Pediatric Evaluation of Disability Inventory (PEDI; PEDI-CAT), the School Function Assessment (SFA), and the Participation and Environment Measure for Children and Youth (PEM-CY).

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Teaching General Education Preservice Teachers Special Education Acronyms using SAFMEDS Renée E. Lastrapes, Ph.D. University of Houston-Clear Lake Abstract Newly minted general education teachers will encounter issues regarding students with disabilities that teachers need to be made aware of in their training programs. Most importantly, general education teachers will be the one most likely to identify struggling students. It is therefore necessary that preservice teachers are taught in their training programs not only what the different disability categories are and their corresponding characteristics, but also the language of special education that they will encounter on the job, as well as research-based strategies that are designed to help struggling students. In this study, two face-to-face sections of an undergraduate survey of exceptionalities class were taught to self-monitor their knowledge of acronyms used in special education using one of two strategies; Say-All-Fast-Minute-EveryDay-Shuffled (SAFMEDS) or online quizzes. The SAFMEDS group showed stronger achievement on the posttest than the online quiz group. Social validity data indicated positive student perspectives toward both interventions with negative reactions to the length of time for both procedures, with both groups indicating that they would have liked a longer period of time to complete the task. Both groups also indicated that they liked using progress monitoring and all said they would use both interventions with their future classrooms. Teaching General Education Preservice Teachers Special Education Acronyms using SAFMEDS General education teachers, especially in elementary schools, continue to play an increasingly active role in the identification of students with disabilities, especially high incidence disabilities. According to the National Center for Education Statistics (NCES, 2016), the number of students receiving special education services increased from 4.7 million (11%) of children age 3-21 in 1990 to 6.6 million (13%) in 2014. Federal law recognizes 13 different disability categories that are eligible for special education services. These categories can be considered low- or highincidence where high incidence disabilities account for approximately 80% of the student population, are considered specific learning disabilities, speech and language impairments, emotional disturbance and mild intellectual disabilities (Friend & Bursuck, 2015). Low incidence disabilities occur in less than 20% of the population and are: intellectual disability, multiple disabilities, hearing impairment, orthopedic impairment, other health impairment, visual impairment, autism deaf-blindness, traumatic brain injury, and developmental delay (Friend & Bursuck, 2015). Students diagnosed with high incidence disabilities will be taught and identified in their general education classes with the help of a team approach. The reauthorization of the Individuals with Disabilities Education Act (IDEA) of 2004 changed the way that students may be identified as having a disability, moving away from the discrepancy model and toward a response to intervention (RTI) approach (Fletcher et al., 2004). In the RTI model, general education teachers play a much larger role, not only in identifying students who may need intervention, but also in 93

implementing research-based educational and behavioral practices and monitoring students’ academic progress (Chiang et al., 2013). When novice teachers begin teaching, they will be expected to participate in the RTI process and to monitor their students’ progress to make data-driven decisions about academic and behavioral instruction. Data-based decision making, where teachers use assessment data to make instructional decisions, is the goal of progress monitoring (Stecker et al., 2008). Teachers must also be proficient at using data to evaluate the effectiveness of their instruction and must be able to interpret their data-based decisions to parents and colleagues (Wagner et al., 2017). In order to be effective at their jobs and to recognize possible academic or behavioral problems their future students may display; preservice general education teachers need to be knowledgeable of the types of disabilities they may encounter in their students as well as competence in speaking and understanding the “language of special education” (Doran, 2013). Special education has long been known for its use of specialized vocabulary (Doran, 2013), especially its use of acronyms. New general education teachers need to be cognizant of this vocabulary (Diliberto & Brewer, 2012). Terms such as LRE (least restrictive environment), FAPE (free and appropriate public education), and the various disability categories (e.g., SLDspecific learning disability, OHI-other health impaired, EBD-emotional and behavioral disorder, etc.) necessitate direct instruction of these terms to general education teachers, not only to facilitate the collaboration between general and special education teachers but also to assist general education teachers in communication with parents of children for whom there is a suspected disability. Teachers need to be knowledgeable about the characteristics of students with disabilities and research-based strategies that are best suited to teach students with disabilities in order to better identify and serve these students in an inclusive environment. Research-Based Practices and Practice-Based Teaching The idea of using research as a basis for what teachers teach stems from the medical community using research-based decisions for medical interventions (Slavin, 2002). Even though the use of research-based practices is now mandated in the Every Student Succeeds Act (ESSA, 2016) and the use of “best practices” is recommended in the reauthorization of IDEA (2004), many teachers are unfamiliar with empirically-based behavioral and academic interventions for students, including students diagnosed with disabilities (Guckert et al., 2016). Exacerbating this problem is the fact that while current laws require the use of research-based practices, few general and special educators can provide accurate definitions of what a research-based practice is, with most stating that they learned of them at the school level (and not in their teacher training programs) and that lack of training, time, and materials were barriers to utilizing these practices in their teaching (Bradley-Black, 2013; Guckert et al., 2016). It is important that teachers are knowledgeable and competent with academic strategies that stem from research given that general education teachers will typically be the first to identify students with potential highincidence disabilities such as specific learning disabilities. Precision Teaching and SAFMEDS Precision teaching (PT) is defined as a system of teaching used in Applied Behavior Analysis that uses systematic gathering of data to evaluate instructional strategies and to guide curricular decisions (Kubina & Yurich, 2012). Precision teaching emerged from the field of behavior analysis (Lindsley, 1971) and stems from the idea that fluent behavior is inherently different 94

from non-fluent behavior; namely, that the fluency of a behavior is a dimension of the behavior, and not just a measure of that behavior. This teaching method is used to evaluate instruction and can be applied to any content (West & Young, 1992); it is a continuous monitoring of selfprogress that focuses on observable behavior, measures performance via frequency, and should be altered if the student isn’t making progress (Lindsley, 1990a). According to Lindsley, “the ‘precision teacher’ performs like a coach, an advisor, and an on-line instructional designer. She arranges materials and methods for the students to teach themselves, including self-counting, timing, charting, and one-on-one direction and support” (1992, p. 51). In this way, precision teaching connects systematic formative assessment and data-based decision-making, which are both research-based instructional programs (Deno, 2016). For this paper, a specific precision teaching method called “Say-All-Fast-Minute-Every-DayShuffled” was investigated (SAFMEDS; Graf, 1994). SAFMEDS is a technique where students create flashcards with a key term on one side and the definition on the other. The SAFMEDS procedure differs from traditional flashcards in that they are practiced at regular intervals (daily or weekly), are timed, and student progress is monitored. SAMFEDS was initially created for use in a special education setting (Lindsley, 1990b). The SAFMEDS procedure has been used in both the K-12 setting (Beverley et al., 2016) and in higher education (Beverley et al., 2009). The types of delivery have also been studied, namely, using student-created flashcards versus instructorprovided (Cihon et al., 2012) as well as computer quizzes (McDade et al., 1985). A recent review of the SAFMEDS literature indicated that the steps in the SAFMEDS procedure lack consistent operational procedures (Quigley et al., 2017); however, the steps used in the present study will be described. The present study was conducted to determine the effectiveness of teaching preservice teachers research-based interventions by implementing the research-based interventions with their own coursework. Another focus of the study was to determine the most effective way to instruct acronyms frequently used in special education to a preservice undergraduate survey of exceptionalities class. Two sections of the same course were used to compare SAFMEDS or a researcher-created online quiz that students completed weekly to determine which was more effective at learning the acronyms, as well as to determine the students’ perceptions of the interventions they utilized. The research questions addressed were: Do students who are quizzed weekly on special education acronyms using one-minute SAFMEDS perform better than students who complete an online acronym one-minute quiz weekly? What are the students’ perceptions of the respective intervention used with their group? What are the students’ perceptions of progress monitoring their own achievement on the acronyms? Method Setting and Participants For this study, students in a face-to-face survey of exceptionalities undergraduate course at a small teaching university in the southern United States were utilized. The course is designed for students who are receiving their certification in general education, either early childhood through 6th grade generalist certification or 7th-12th grade, subject-specific certification. These students had already taken an introduction to exceptionalities course, and this was an extension 95

emphasizing both effective methodologies to use when teaching in inclusive classrooms that contain students with disabilities, as well as how to make effective instructional decisions for diverse learners. The data collection tool was either the SAFMEDS intervention or randomized quizzes administered online via the course management software (Blackboard). Both sections were taught how to progress monitor their own scores on their respective interventions. Thirty-four participants from two classes completed the interventions, but due to missing pre- or post-test scores, three were removed from the overall analysis leaving a total of 31 qualified participants. The two classes consisted of only juniors and seniors. Demographic variables are presented in Table 1. Table 1 Demographic Characteristics of Participants SAFMEDS N % Gender Female 10 76.9 Male 3 23.1 Ethnicity Asian 1 7.7 Black/African-American 2 15.4 Hispanic 6 46.2 White 4 30.8 Grade Level Certification Elementary 8 61.5 Secondary 5 38.5

Online Quiz N %

Total N

%

16 2

88.9 11.1

26 5

83.9 16.1

0 1 10 7

0 5.6 55.6 38.9

1 3 16 11

3.2 9.7 51.6 35.5

16 2

89.9 11.1

24 7

77.4 22.6

Procedure As part of the course, students were expected to become familiar with acronyms used frequently in special education. During the first week of class, both classes were given a pre-test of 50 special education acronyms such as SLD (specific learning disability) and FBA (functional behavioral assessment) that they would encounter in their schools. These acronyms were part of the course material taught to all sections of the course and were aligned with the state education agency standards. The quiz was simply the acronym with a blank; if they knew or had heard the acronym before, they were asked to fill in the blank with its meaning to determine their overall familiarity with special education terminology. At the toss of a coin, the SAFMEDS and the online treatment conditions were randomly assigned to the two sections. The SAFMEDS treatment was assigned to a morning section of the class (9:00-11:50 AM) and the online treatment was assigned to the evening section (7:00-9:50 PM), the researcher was the instructor in both courses. Once the pretest had been given, the students in the SAFMEDS section were given a handout with all the acronyms with their corresponding meanings. The term SAFMEDS was discussed with the class and the procedure was explained. The class was given the homework assignment to come the following week with index cards in a Ziploc bag that could be carried with them to class for the remainder of the semester. On the index cards, they were told to write the acronym on one side and the meaning on the other. They 96

would be given a few minutes at the start of the class period to study their cards or acronym sheet and then quiz themselves on the terms. They were given the following instructions. 1. 2. 3. 4.

Set the timer on their smart phone for one minute. Start the timer. Read the acronym on one side and say the term out loud (quietly, to themselves). Check their answer, if they got it right, put the card in one pile, if they got it wrong put in another pile. 5. When the timer goes off, count the cards in each pile. 6. Chart their progress on the progress monitoring sheet. 7. Shuffle and put away. Both classes had been trained in how to fill out the progress-monitoring sheet and were told to log their correct answers with a dot and incorrect answers with an X (see Figure 1). The final step was to shuffle the cards and then put them away for the next class period. This whole procedure took approximately five to ten minutes and became part of the class routine. They were told that they would receive a participation grade for completing the SAFMEDS weekly. No formal treatment fidelity measures were recorded but the researcher observed students weekly performing the task with fidelity.

Figure 1. Progress-monitoring sheet used for Both SAFMEDS and Online Quiz Conditions: Weeks on the x axis, number correct/incorrect on the y axis. The online treatment classroom was also given the list of acronyms and their meanings, and were told to study the list before attempting the quiz weekly outside of class. They were taught how to use the progress-monitoring sheet and were told that when they take their weekly quiz they would chart their progress. Blackboard, the course management software, was used to create a quiz where the acronym was presented and the students had to choose one of four possible 97

meanings. Care had to be taken to ensure that the choices were believable enough. Table 2 displays the correct answer with the three distractors for a subset of the overall quiz. Table 2 Acronym Online Quiz and Multiple-Choice Answers Acronym Correct Answer Choice 2

Choice 3

Choice 4

OHI

Other Health Impairment

Other Health Insurance

Organizational Health Inventory

Optimum Health Instruction

FAPE

Free and Appropriate Public Education

Families and Advocates Partners for Education

Fund for Assistance of Private Education

Free Applicable Public Education

IEP

Individualized Education Program

Individuals with Emotional Problems

Intensive Educational Program

Initial Enrollment Period

When the students clicked on the link, the randomized quiz was presented to them. They were given 60 seconds to take the quiz and when finished were presented with the number they got correct and the number attempted. Students were asked to take one quiz per week and to record their progress on the progress-monitoring sheet. Students were told that their quiz scores would not be included in the final grade, but they would receive participation points for completing the quizzes. At the end of the course each class was retested with the same 50-item acronym quiz used in the pretest. The online quiz section was also instructed in SAFMEDS, so that they would be knowledgeable of that strategy to use in their future classrooms. Student Perceptions of the Intervention After the conclusion of the semester, both sections were contacted via email and asked to provide feedback to the following questions “Did you like learning the acronyms in special education using SAFMEDS/online quizzes? If you did, why? If you didn't, why not? Did you like using the progress-monitoring sheet? If you did, why? If you didn’t, why not? Also, please tell me if you think you would use these techniques in your own classroom when you become a teacher? If so, why? If not, why not?” Data Collection and Analysis The pre-test and post-test were scored by the author and checked by a colleague, and were inputted into an Excel spreadsheet, and later the data were analyzed using the Statistical Package for the Social Sciences (SPSS). The participants were labeled to their treatment condition and the scores were compared using a two-way repeated measures analysis of variance (ANOVA). The perception data were analyzed for common and diverging answers to the questions posed.

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Results Instructional Method Descriptive statistics for the pre- and post-test are presented in Table 3. A two-way repeated measures ANOVA was conducted to determine the effect of the two treatments over time on the acronym quizzes. Assumptions of repeated measures ANOVA were assessed. Table 3 Descriptive Statistics for the Pre- and Post-Tests SAFMEDS Online Quiz Mean SD Mean SD Pretest 3.62 3.124 3.22 2.264 Posttest 20.77 8.899 11.67 7.244 The assumption of normality was violated; however, repeated measures ANOVA is robust to violations of normality (Rovai et al., 2013). Sphericity, which shows that the variances of the differences between all possible pairs of pre- and post-tests are equal was established for the interaction term, assessed by Mauchly’s test (p >.05), and Box’s test showed equality of covariance matrices (p >.05). There was a significant interaction between treatment and time on the quiz grades F(1, 29) = 11.43, p < .01, partial eta squared = .28 indicating that the two groups scored significantly differently depending on the time they were tested. This can be seen in Figure 2 which shows the estimated marginal means for the two groups on the pre- and post-test while controlling for time. Examining the main effects, there was no significant difference on the pre-test, t(1) = .40, p > .05 but there was for the post-test t(1) = 3.2, p < .05.

Figure 2. Estimated Marginal Means by Group. The SAFMEDS group showed significant gains in the post-test scores. 99

Student Perceptions of the Intervention Only ten students responded to the follow-up email; six students from the SAFMEDS group and 4 from the online group. Overall, the SAFMEDS group expressed slightly more favorable opinions of the assessment process than the online quiz group. All six students reported favorable attitudes to the SAFMEDS procedure and three of the four reported favorable attitudes to the online quizzes. One student wrote, “I did like doing the SAFMEDS every week because it was a way to track my progress and it made me accountable for my learning. I wasn’t just trying to memorize the information.” One student who did not like the online quizzes said, “The online quizzes were rough at first, and I felt like they were pointless given that it was impossible to truly succeed.” Both groups indicated that they would use their respective procedure in their future classrooms. Both groups took issue with the limit of one minute per procedure. From the SAFMEDS group one student said, “The only thing I did not like was the fact that I was trying to beat the oneminute timer. I cannot concentrate knowing that my time was almost over…when it comes to timed quizzes, I freeze.” In the online quiz group another student said: I think the activity would be better if we had two minutes each quiz, there are 50 terms and I could never get past 15, so max score someone should be able to get is around 30 and that seems like it would provide a lot more data in my opinion. Both groups indicated that they saw value in the progress-monitoring and that they would indeed use it in their future classrooms. From the SAFMEDS group, a student commented: With the progress-monitoring, I got to see the progress I was making each week, I found it to be helpful, I would definitely use a technique like that in my classroom, it showed you your progress and you could see the areas that you needed to work on. From the online quiz group, a student shared, “I liked the progress monitoring and the fact that it was just a minute a week. I think this is something I would use in my classroom.” In general, students’ perceptions were positive. Both groups indicated that they would use both methods of instruction with their own future classrooms. Also, both classes stated that they could see the utility of the progress monitoring process, and that they would definitely use it with their own classes in the future. Discussion Using research to inform practice has long been recognized as a need in education and is increasingly so, given the inclusion of greater numbers of students with disabilities in the general education setting. Overall, the findings of this study indicated that SAFMEDS were more effective in students’ achievement of special education acronyms than the online quizzes. This is different than the findings of McDade et al. (1985) which found SAFMEDS equally effective as the computer intervention, although their sample size was very small. Findings here are consistent with SAFMEDS versus “treatment as usual” in an undergraduate statistics class, where the students who were assigned SAFMEDS to learn statistical terms achieved at a higher rate than the group who were taught in the normal fashion (Beverly et al., 2009), as well as 100

second grade students who learned more Welsh vocabulary words using SAFMEDS than their “treatment as usual peers” (Beverly et al., 2016). It is important to note that the tasks assigned to both classes were only assigned a participation grade, and that there were students who did not complete all assessments at every class. In reviewing attendance data and online quiz data via the course management system, the SAFMEDS class completed 90% of all assessments as a group and the online quiz class completed 88% of all assessments as a group. Due to the similarities in the number of assessments completed, it was felt that the groups were similar enough to be compared. This study introduced preservice teachers to several responsibilities they will encounter in their first years of teaching: the use of progress monitoring for data-based decision making which is implemented in RTI, as well as a research-based strategy they can use to teach vocabulary or math facts to their classrooms. These preservice teachers will attend Individualize Education Plan (IEP) meetings with their students’ parents as well as other teachers, and they will be better equipped to understand and utilize the many acronyms that are common in special education. By practicing the research-based strategies intended to support students with exceptionalities in their own coursework, preservice teachers got to experience first-hand these strategies, reinforcing the likelihood that they will use them with their own future students. Schön (1995) states that “we should ask not only how practitioners can better apply the results of academic research, but what kinds of knowing are already embedded in competent practice” (p. 29). By embedding the research-based practices into the practice of teaching the material for the course, the students had the opportunity to learn course material by implementing the research-based strategies they will not only encounter as professionals (progress monitoring and data-based decision making) but one section learned the acronyms with a technique that they can then, in turn, use with their students (SAFMEDS). The online quiz section was instructed in the implementation of SAFMEDS at the end of the course, so it has been added to their “toolbox” of research-based strategies to use with their own classrooms. By conducting this study within a classroom to show preservice teachers how to utilize research-based strategies by teaching course material by using those same strategies, an attempt was made to bridge the research-topractice gap between institutions of higher learning and K-12 schools as described by Davis (2007). Limitations The findings of the present study should be viewed with limitations. This study pertained to only two undergraduate courses for general education teachers at one small teaching university. While both sections were taught progress monitoring using practice-based instruction, namely by monitoring of their own progress in the acronym material, only one section was taught the research-based intervention of SAFMEDS by using SAFMEDS to learn material, the other section was taught using online quizzes. Another limitation is the fact that this study is very small in size and limited to the demographic makeup specific to the university where it was conducted, as well as to the teaching style of the researcher who presented the material. Suggestions for Future Research The results of the present study have implications for future research. University professors at the undergraduate and graduate level should utilize the strategies they want their students to learn by teaching course material by utilizing those same strategies. This creates a model for the 101

students to emulate. Future research should compare the proficiency of this approach across more strategies that are research-based, such as Collaborative Strategic Reading (Boardman et al., 2012) and Classwide Peer Tutoring (Delquadri et al., 1986). Also, research should focus on this approach across classrooms of preservice teachers with a general education or special education focus, to determine if there are similarities or differences in the different teacher populations. The results of the present study provide some evidence that utilizing research-based strategies in an undergraduate class to teach course material can increase the use of those strategies by preservice teachers when they get their own classrooms. This has the potential to decrease the research-to-practice gap that has been noted in the literature. The present study showed that, for this sample, SAFMEDS was a more effective strategy to teach acronyms than online quizzes. Students’ perceptions of the practices point to the effectiveness of using research-based interventions to teach course material in undergraduate courses with the goal to promote the use of those research-based strategies when these preservice teachers go on to teach their own classroom. By utilizing this approach, university professors can potentially increase the likelihood that new teachers will utilize the practices they themselves have used in their own learning in class. References Beverley, M., Hughes, J. C., & Hastings, R. P. (2009). What’s the probability of that? Using SAFMEDS to increase undergraduate success with statistical concepts. European Journal of Behavior Analysis, 10(2), 235-247. Beverley, M., Hughes, J. C., & Hastings, R. P. (2016). Using SAFMEDS to assist language learners to acquire second-language vocabulary. European Journal of Behavior Analysis, 17(2), 131-141. Boardman, A. G., Swanson, E., Klingner, J. K., & Vaughn, S. (2012). Using collaborative strategic reading to improve reading comprehension. In B. G. Cook & M. Tankersley (Eds.), Research-based practices in special education (pp. 33–46). New York, NY: Pearson Education. Bradley-Black, K. H. (2013). Teachers and evidence-based practices (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3562265) Chiang, B., Russ, S. L., & Skoning, S. N. (2013). Best practices in assessment for eligibility identification. In B. G. Cook & M. Tankersley (Eds.), Research-based practices in special education (pp. 249-259). Boston, MA: Pearson. Cihon, T. M., Sturtz, A. M., & Eshleman, J. (2012). The effects of instructor-provided or student-created flashcards with weekly, one-minute timings on unit quiz scores in introduction to applied behavior analysis courses. European Journal of Behavior Analysis, 13(1), 47-57. Davis, S. H. (2007). Bridging the gap between research and practice: what's good, what's bad, and how can one be sure? Phi Delta Kappan, 88(8), 569-578. Delquadri, J., Greenwood, C. R., Whorton, D., Carta, J. J., & Hall, R. V. (1986). Classwide peer tutoring. Exceptional Children, 52, 535–542. Deno, S. L. (2016). Data-based decision-making. In S. R. Jimerson, M. K. Burns, & A. M. VanDerHeyden (Eds.), Handbook of Response to Intervention: The science and practice 102

of assessment and intervention. 2nd ed. (pp. 9-28). Springer Science & Business Media. DOI: 10.1007/978-1-4899-7568-3 Diliberto, J. A., & Brewer, D. (2012). Six tips for successful IEP meetings. Teaching Exceptional Children, 44(4), 30-37. Doran, P. R. (2013). Parentally placed students in private schools: A brief review of United States policy and practice. Journal of the International Association of Special Education, 14(1), 79-86. Every Student Succeeds Act Accountability, State Plans, and Data Reporting: Summary of Final Regulations (20016). Retrieved from: https://www2.ed.gov/policy/elsec/leg/essa/essafactsheet170103.pdf Fletcher, J. M., Coulter, W. A., Reschly, D. J., & Vaughn, S. (2004). Alternative approaches to the definition and identification of learning disabilities: Some questions and answers. Annals of dyslexia, 54(2), 304-331. Friend, M., & Bursuck, W. D. (2015). Including students with special needs. Upper Saddle River, NJ, USA: Pearson, Inc. Graf, S. A. (1994). How to develop, produce and use SAFMEDS in education and training. Youngstown, OH: Zero Brothers Software. Guckert, M., Mastropieri, M. A., & Scruggs, T. E. (2016). Personalizing research: Special educators’ awareness of evidence-based practice. Exceptionality, 24(2), 63-78. Individuals with Disabilities Education Act Amendments of 2004, Pub. L. No. 108–446, 20 U.S.C. § 1400 et seq. Kubina, R. M., Yurich, K. K. L. (2012). The precision teaching book. Lemont, PA: Greatness Achieved Publishing Company. Lindsley, O. R. (1971). Precision teaching in perspective: An interview with Ogden R. Lindsley. Teaching Exceptional Children, 3, 114-119. Lindsley, O. R. (1990a). Our aims, discoveries, failures, and problem. Journal of Precision Teaching, 7, 7-17. Lindsley, O. R. (1990b). Precision teaching: By teachers for children. Teaching Exceptional Children, 22(3), 10-15. Lindsley, O. R. (1992). Precision teaching: Discoveries and effects. Journal of Applied Behavior Analysis, 25(1), 51-57. McDade, C.E., Austin, D.M., & Olander, C.P. (1985). Technological advances in precision teaching: A comparison between computer-testing and SAFMEDS. Journal of Precision Teaching, 6(3), 49-53. National Center for Education Statistics [NCES]. (2016). The condition of education: Children and youth with disabilities. Retrieved from: https://nces.ed.gov/programs/coe/indicator_cgg.asp Quigley, S. P., Peterson, S. M., Frieder, J. E., & Peck, K. M. (2017). A Review of SAFMEDS: Evidence for Procedures, Outcomes and Directions for Future Research. The Behavior Analyst, Published online. 1-19. Rovai, A. P., Baker, J. D., & Ponton, M. K. (2013). Social science research design and statistics: A practitioner's guide to research methods and IBM SPSS. Chesapeak, VA: Watertree Press LLC. Schön, D. A. (1995). Knowing-in-action: The new scholarship requires a new epistemology. Change: The Magazine of Higher Learning, 27(6), 27-34. Slavin, R. E. (2002). Evidence-based education policies: Transforming educational practice and research. Educational researcher, 31(7), 15-21. 103

Stecker, P. M., Fuchs, D., & Fuchs, L. S. (2008). Progress monitoring as essential practice within response to intervention. Rural Special Education Quarterly, 27(4), 10. Wagner, D. L., Hammerschmidt‐Snidarich, S. M., Espin, C. A., Seifert, K., & McMaster, K. L. (2017). Pre‐service Teachers’ Interpretation of CBM Progress Monitoring Data. Learning Disabilities Research & Practice, 32(1), 22-31. West, R. P., & Young, K. R. (1992). Precision teaching. In R. P. West & L. A. Hamerlynck (Eds.), Designs for excellence in education: The legacy of B. F. Skinner (pp. 113-146). Longmont, CO: Sopris West, Inc. About the Author Renée E. Lastrapes, Ph.D. is an associate professor at the University of Houston-Clear Lake. She is a quantitative methodologist and works with doctoral students, teaching research methods and statistics. Prior to entering higher education she taught special education (elementary, middle, and high school) for 16 years. Her research interests are in the area of behavioral interventions for children with emotional and behavioral disorders as well as quantitative research methods.

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General Education and Special Education Teacher Perspectives on Integrated Co-Teaching Jordan McCaw, Ed.D Massapequa Union Free School District Stony Brook University Abstract The integrated co-teaching model continues to gain momentum around the country with many school districts adding this model to their schools’ continuum of services. Historically, the special class, formerly known as “self-contained,” was an appropriate placement for students in need of specially designed instruction. In contrast, the integrated co-teaching model of instruction is unique because students with disabilities are provided with specially designed instruction within the general education environment. This collective case study involved individual general education and special education teacher interviews, two focus groups, and instructional observations. This paper investigates how general education and special education teachers defined the most effective model of integrated co-teaching. Additionally, this paper explores to what extent teachers’ actions and practices were consistent with the research of Cook and Friend (1995). Findings address reports of a power dynamic between teachers that was best mitigated by differentiation of instruction. Data were inconsistent between general education and special education teachers. Moreover, there were several unifying findings related to structural differences in service delivery based on grade level. Finally, the scheduling of planning time was reported as a significant challenge. Keywords: integrated co-teaching, co-teaching, co-planning, ICT General Education and Special Education Teacher Perspectives on Integrated Co-Teaching Integrated co-teaching (ICT) is a highly popular instructional model for teaching students with disabilities together with their nondisabled peers. This model is widely used among elementary and secondary schools in both urban and suburban areas. This study explored how general education and special education teachers defined the most effective model of ICT and to what extent their practices reflected the practices delineated in the foundational research of Cook and Friend (1995). Cook and Friend framed co-teaching as a model that promotes inclusion of students with disabilities, and teaching approaches that effectively reduce the student-to-teacher ratio. ICT program delivery varies from district to district and state to state. This study was designed to elicit the insight and perspective of general education and special education teachers in a large suburban school district. Literature Review Legal, Legislative, and Regulatory Context In the 1950s, middle and high schools were reorganized based on a national teacher shortage. To address this, team teaching was introduced as a general education instructional delivery model. This approach involved shared responsibilities for large group lectures/presentations and the 105

small group sessions that followed (Friend et al., 1993, p. 15). As the first form of co-teaching, team teaching was best defined by Trump (1966): The term “team teaching” applies to an arrangement in which two or more teachers and their assistants, taking advantage of their respective competencies, plan, instruct, and evaluate in one or more subject areas a group of elementary or secondary students equivalent in size to two or more conventional classes, using a variety of technical aids to teaching and learning through large group instruction and small group discussions (p. 7). In the 1960s and 1970s, the model continued to evolve in K-12 schools and included several adaptations. One involved two general education teachers planning together but delivering instruction separately. As a model utilized in middle school and in some high schools, team teaching provided students with enriched individualized learning experiences and enabled teachers to provide instruction through a professional system of support (Cook & Friend, 1995, p. 15). Although “co-teaching” as a strategy has been utilized since the 1950s, considerable research on the co-teaching instructional model for students with disabilities (SWD) only began in the early 1990s. The passage of the Rehabilitation Act, No Child Left Behind, the Individuals with Disabilities Education Act and the Elementary and Secondary Education Act helped shape the conversation about how to most appropriately support SWD. From this context, co-teaching has been studied in K-12 settings around the United States and the world. According to a synthesis of the literature, co-teaching developed into a service delivery vehicle offered through special education. Gately & Gately (1993) defined co-teaching as “collaboration between regular classroom teacher and special educator for all of the teaching responsibilities of all students assigned to a classroom” (p. 4). In this classroom setting, collaborative instruction ensures that SWD are meeting with success. Ultimately, if SWD are included in the general education setting and expected to master standards, then both general education teachers and special educators must work together on all elements of instruction so that all students-disabled and nondisabled alike- achieve success (Cook & Friend, 1995; Murawski & Dieker, 2003). There are four key pieces of legislation that propelled the implementation of the ICT model in the American public school system (Darrow, 2019; Klein, 2018; Taylor 2011). The Rehabilitation Act of 1973, the No Child Left Behind Act, the Individuals with Disabilities Education Act and the Every Student Succeeds Act prioritized the least restrictive environment and thus the advent of the ICT model designed for students with disabilities. ICT provided an avenue through which special education instruction could occur within a general education environment.

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Table 1 Landmark Special Education Legislation Legislation

Description

The Rehabilitation Act of 1973

Provided specific requirements for schools that receive federal funds to ensure that students with disabilities receive reasonable accommodations that enable them to fully benefit from the public education experience (Taylor, 2011).

Individuals with Disabilities Education Act (IDEA)

Under IDEA, all students were entitled to a free and appropriate public education and were guaranteed to receive an education in the least restrictive environment. The reauthorization of IDEA in 2004 stressed access to the general education curriculum for students with disabilities and accountability through high-stakes testing (Isherwood & Barger-Anderson 2007; Scruggs et al., 2007). In the United States, over 61% of all students with disabilities aged 6 through 21 receive the majority of their education in general education settings as the LRE (U.S. Department of Education, 2010).

No Child Left Behind (NCLB)

The NCLB legislation, which was enacted in 2001 and signed into law in 2002, placed an emphasis on special education and the need for more effective collaboration and co-teaching models. The law was an update to the Elementary and Secondary Education Act and expanded the role of the federal government in oversight of student outcomes (Klein, 2018). The NCLB Act created regulation that prompted the expansion of the co-teaching model. Moreover, it underscored the importance of special education students meeting the requirements of the general education curriculum.

Every Student Succeeds Act (ESSA)

The ESSA was signed into law in 2015. The law reauthorized the 50-year-old Elementary and Secondary Education Act and emphasized equal opportunity for all students. The NCLB Act (enacted in 2002) was a major step for American students as it focused on the importance of progress and the need for additional support irrespective of race, income, zip code, disability, language, or background. The 2015 law emphasized fully preparing all students for success in college and career. Special educators contend that the ESSA, which grants significantly more power to states while continuing to require reporting from schools about the capabilities of their students, is a step in the right direction for all students, especially those with disabilities (Darrow, 2016, p. 41).

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Co-Teaching Co-teaching is a service-delivery model in which one general education teacher and one special education teacher share responsibilities including for planning and delivering instruction to special education students and their general education peers. Co-teaching must unite the science of specially designed instruction and effective pedagogy with the art of reorganizing resources and schedules to provide students with disabilities with better opportunities to be successful in learning what they need to learn (Friend, Cook, Hurley-Chamberlain, & Shamberger, 2010). Similar to Friend et al., Sileo and van Garderen (2010) describe co-teaching as “an instructional delivery model applicable to teaching students with disabilities in the least restrictive integrated classroom settings in which general and special educators share responsibility for planning, delivering and evaluating instructional practices for all students” (p. 14). In this setup, “a special education teacher works along with the general education teacher to provide needed supports, precluding the need for students with disabilities to leave the classroom to receive specialized assistance” (Solis, Vaughn, Swanson, et al., 2012, pp. 498- 9). This arrangement gives students the opportunity to remain in the general education setting while receiving instructional assistance from a special education teacher. Collaborative Partnership and Systemic Challenges Historically, students with individualized educational programs (IEPs) were placed in a more restrictive model including special class with a small student-to-teacher ratio or in a less restrictive model such as a general education setting with related services. To provide students with disabilities with access to the general education curriculum, co-teaching relegates two teachers to a classroom: a general education teacher as the content specialist and a special education teacher as a specialist in differentiated instruction. While co-teaching is a powerful instructional vehicle, the model itself can also be a challenge for a variety of reasons. For example, teachers have expressed frustration when students fail to meet their expectations (McMullen, Shippen, & Dangel, 2012). However, appropriate support services such as instructional materials, equipment, and access to specialized personnel tended to assist in alleviating the concerns often expressed by general education teachers about their students’ success with inclusion models (Avramidis & Norwich, 2002). The Exemplar Integrated Co-Teaching Model Marilyn Friend and Lynne Cook (1995) effectively defined the ICT model. These authors wrote extensively on the subject in the 1990s and early 2000s, and their research forms the framework of the model, which is predicated on six approaches to co-teaching including one teach, one assist; one teach, one observe; station teaching; parallel teaching; alternative teaching; and team teaching. Research in the area of ICT relies heavily on the Cook and Friend (1995) model and its core approaches and strategies form the basis for most studies conducted during the last 25 years. Districts that first develop and implement ICT models often refer to the Cook and Friend (1995) standard as the basis upon which their models are built. The six co-teaching approaches involve utilizing two certificated teachers (one certified in general education and the other certified in special education), a shared classroom, and instructing a mixed group of students. Most Effective Model of ICT For the purpose of this collective case study, the most effective ICT model was defined by three characteristics, as outlined in the research of Friend and Cook: 108

● Two certificated educators delivering instruction to a group of blended students in one location (Cook & Friend, 1995). ● Shared responsibility for the previously mentioned group of students in relation to content and instructional objectives, which involves mutual accountability (Cook & Friend, 1995). ● Regular use of the six co-teaching approaches (one teach, one assist; one teach, one observe; station teaching; parallel teaching; alternative teaching; and team teaching) to address specific IEP goals for students with disabilities and the needs of nondisabled students (Friend et al., 2010, p. 12). These three considerations, which are related to successful collaborative co-teaching partnerships, were selected because they are the core underpinning of effective ICT. Methods This study involved a collective case study where the one issue or concern was selected with multiple cases to illustrate the issue (Creswell & Poth, 2017, p. 99). Collective case studies involve extensive study of several instrumental cases, intended to allow better understanding, insight, or perhaps improved ability to theorize about a broader context (Lune & Berg, 2017, p. 165). This study involved the selection of four co-teaching teams who delivered co-taught instruction daily. These teams were interviewed in private offices and observed in the classroom setting. Additionally, there were two focus groups—one consisting of general education teachers and the other consisting of special education teachers. The focus groups included ICT teachers who were not interviewed or observed. There were sixteen participants in this study. Three steps were used to gather evidence: (1) teacher interviews, (2) focus groups, and (3) observations. This study was proposed, reviewed, and approved by the Hofstra University Institutional Review Board and the data for this study originated from a larger study by McCaw (2019), for a doctoral dissertation at Hofstra University. The research questions that undergirded this study were 1. How did general education and special education teachers define the most effective model of ICT? 2. To what extent were their instructional practices consistent with the Cook and Friend model (1995)? Field Setting This study’s setting was a suburban school district located on Long Island with an enrollment of over 7,000 students from kindergarten through 12th grade. The District also has several students who qualify for New York State Alternative Assessment and who will remain in school until the age of 21. According to data from the New York State Report Card, 10% of the student population qualifies for free or reduced lunch. Also, 10% are considered students with disabilities (NYS Report Card, 2019). This study involved teacher teams assigned to two buildings: an elementary school with over 600 students that has approximately 50 students with disabilities and a high school with approximately 1,700 students with over 200 students with disabilities. 109

Participant Selection Purposive sampling was utilized to recruit teachers who had previously obtained tenure and who had the most experience co-teaching. Additionally, consideration was given to those who had the most longevity with their current co-teaching partner. Merriam (1998) stated, “purposive sampling is based on the assumption that the investigator wants to discover, understand, and gain insight and therefore must select a sample from which the most can be learned” (p. 61). Collecting data from four different co-teaching teams—two at the elementary level, and two at the secondary level—was an appropriate sample. According to Marshall (1996), “an appropriate sample size for a qualitative study is one that adequately answers the research question” (p. 523). In order to elicit interest, the researcher sent a recruitment letter to tenured teachers identified by school district administration as integrated co-teachers who were then selected based on 1) their tenure status, 2) their ICT experience as compared with their peers in the school, and 3) their ICT experience with their current co-teaching partners. Moreover, participants were both male and female and were from somewhat diverse professional backgrounds. Data collection for the study began in July of 2018 and concluded in November of 2018. Table 2 General Education and Special Education Teacher Participants Study Participants by Category # of Interview Participants

# of Focus Group Participants

General Education Teachers

4

4

Special Education Teachers

4

# of Observations

3* 4

*One observation of an elementary ICT class did not occur as planned.

Procedures Interviews There was a total of eight separate interviews held; four interviews were held with general education teachers and four interviews were held with their special education co-teachers. Interviews were conducted on a 1:1 basis so that each participant could speak freely. Each interview lasted 30-40 minutes and was conducted either in a classroom with no students or in a private office. All interviews were conducted by the researcher. Interview questions are attached as Appendix A. 110

Focus Groups According to Barbour (2008), the focus group “is an interview style designed for small groups of unrelated individuals, focused by an investigator and led in a group discussion on some particular topic or topics” (p. 158). Focus groups are advantageous when the interaction among interviewees will likely produce the best information, when interviewees are similar and cooperative with each other, when time to collect information is limited, and when individuals interviewed one-on-one may be hesitant to give information (Krueger & Casey, 2014). This study involved two focus groups: one consisting of four general education teachers and the other consisting of four special education teachers. The general education teacher focus group consisted of two educators from the elementary level and two educators from the secondary level. The special education teacher focus group consisted of two educators from the elementary level and two educators from the secondary level. Focus group questions for general education teachers are attached as Appendix B. Focus group questions for special education teachers are attached as Appendix C. Observations Creswell and Poth (2017) stated that observation was one of the essential tools for data collection in qualitative research (p. 166). Further, observation is a special skill that necessitates addressing concerns such as the potential deception of interviewees, impression management, and the potential marginality of the researcher in a strange setting (Atkinson, 2015). Three observations – one of the third-grade co-teaching team, and one for each of the high school co-teaching teamswere conducted. Due to an unforeseen scheduling issue, the fourth grade co-teaching team was not observed. The observation protocol is attached as Appendix D. During the lesson, detailed notes were taken on the actions of both teachers, interactions between them, and interactions between and among teachers and students. Data Analysis The procedure for gathering information for this qualitative case study involved collection of data from multiple subgroups. Data analysis was conducted during and after the data collection. Data analysis in qualitative research consists of preparing and organizing the data for analysis; then reducing the data into themes through a process of coding and condensing the codes and finally representing the data in figures, tables, or a discussion (Creswell & Poth, 2017, p. 183). The data for this study were analyzed by subgroups. Each subgroup consisted of one co-teaching pair (a general education content specialist and a special education teacher). Since this study involved four co-teaching teams (a total of eight teachers), there were four subgroups. Data analysis began with data collection through a reflective and analytic journal and with reading the transcriptions of interviews, focus groups, and observations. Creswell and Poth (2017) illuminated one strategy of analysis in case studies is to identify common themes. Based on the research questions and conceptual framework, a first round of primary codes was assigned in order to initiate the three-stage coding process. Key words and phrases that were utilized in the interviews, focus groups, and observations were highlighted. According to Miles, Huberman, and Saldana (2014), “codes are prompts or triggers for deeper reflection on the data’s meaning” (p.73). Coding is thus a data condensation task that enables a researcher to retrieve the most meaningful material, to assemble chunks of data, and to further condense the bulk into readily 111

analyzable units. In this study, the data were reviewed, and codes were created that related specifically to the research questions. This is called deductive coding (p. 81). Each piece of data was then reviewed, and several data reduction steps were performed. First, the researcher highlighted and underlined the data relevant to the different codes. Second, the researcher created and maintained a Microsoft Excel file which included each code and its respective evidence. Third, the researcher re-read the data and adjusted and organized codes to make them more specific and easier to organize the findings. The Dedoose data management tool was utilized to create interactive visualizations and analytics. Finally, the researcher created and drew upon visual thematic representations which helped synthesize the emanating themes, research, findings and the implications to the research questions. The Findings’ section of this study includes key themes that were identified in the data analysis. Findings The findings section elucidates essential themes that came to light during data analysis. The findings answered the two essential research questions: 1. How did general education and special education teachers define the most effective model of ICT? 2. To what extent were their instructional practices consistent with the Cook and Friend model (1995)? General and Special Education Teacher Definition of the Most Effective Model of ICT Table 5 General Education and Special Education Definitions of the Most Effective Model of ICT How Do Teachers Define the Most Effective Model of Integrated Co-Teaching?

General Education Teacher Themes ● ●

Special Education Teacher Themes

Equal partnership Differentiated instruction

● ● roles ●

Co-planning Flexible approach vs. defined Challenges of co-teaching

Summary: General education teachers defined effective ICT as being predicated on participants valuing an equal partnership and on the delivery of differentiated instruction. Special education teachers defined effective ICT as being predicated on co-planning and flexibility. Challenges to the model were noted.

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General Education Teacher Definition of ICT Four general education teachers were interviewed, and another four participated in a general education teacher focus group. Those interviewed included a third-grade teacher, a fourth-grade teacher, a high school teacher of English, and a high school teacher of mathematics. Moreover, the general education teacher focus group included a fourth-grade teacher, a fifth-grade teacher, and two high school teachers of English. As a part of data collection, three classes were observed: a third-grade ICT class, an 11 th-grade ICT English class, and an 11th-grade ICT mathematics class. This study found that general education teachers defined the most effective model of ICT through the themes of equal partnership and differentiated instruction. Equal Partnership Participants globally reported that an equal partnership meant equality of roles. Several teachers emphasized that such equality did not manifest in a “one person as a teacher and the other as a teaching assistant” dynamic. The partnership was interpreted to be akin to marriage. Secondary teachers agreed that their roles (as general education teachers and special education teachers) were interchangeable. In their responses, they underscored that the special education teacher should not uniquely be responsible for students’ modifications and accommodations; rather, both teachers were charged with the delivery of instruction to all students and all responsibilities— specifically, grading and planning—should be shared. Some teachers reported taking the lead but acknowledged the importance of viewing students as individual learners. Even teachers who reported being the focal point of the lesson noted the importance of collaboration and prior planning. Secondary teachers reported that their role within the program “is largely content based” and relegated the special education teacher to be the “scholar of the IEP,” who was skilled in “individualizing instruction.” The majority of teachers agreed that a true model of ICT was one in which an observer would be unable to identify the general education and special education teacher: that is, where all of the responsibilities were “split 50/50,” with both teachers sharing responsibility for students with disabilities and their nondisabled peers. General education teachers additionally reported that in a true model of ICT, the enthusiasm of the partners to be working together should be evident to students and appear to be an “interactive performance.” Elementary general education teachers felt strongly that the special education teacher was responsible for the implementation of the IEP. Differentiated Instruction General education teachers by and large reported that they had varied skillsets and ranges of abilities. They reported that many students are placed in ICT from a special class, formerly known as “self-contained.” Consequently, they felt that small-group work was important. If one teacher was at the board, the other teacher should be actively walking around the room and circulating among students. One high school teacher shared that “The special education teacher focuses more on modifying and differentiation and again, bringing perhaps other interests or other data and content to the classroom.” Access to the general education curriculum resonated among study participants as the primary objective of effective ICT. Teachers conveyed the need to differentiate the approach to meet students at their level of need. Individualized education program goals were an important component of co-taught instruction, and the teachers reported discussing student goals during all their planning sessions. Teachers noted frequent use of graphics organizers, instructional technology, and pre-teaching or re-teaching.

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Special Education Teacher Definition of ICT Four special education teachers were interviewed, and four participated in a special education teacher focus group. Those interviewed included a third-grade special education co-teacher, a fourth-grade special education co-teacher, a high school special education teacher of mathematics, and a high school special education teacher of English. Moreover, the special education teacher focus group included a fourth-grade teacher, a fifth-grade teacher, and two high school special education teachers of English. As a part of data collection, three classes were observed: a third-grade ICT class, an 11 th-grade ICT English class, and an 11th-grade ICT mathematics class. This study found that special education teachers defined the most effective model of co-teaching through the themes of co-planning and flexible instruction. Co-Planning All special educators identified co-planning as a significant component of effective ICT. Teachers conveyed an understanding that tasks needed to be broken down for students and that each co-teacher was responsible for part of the lesson. Elementary teachers agreed that a planned lesson should have defined talking points for each teacher and that co-teaching involved “a great deal of nonverbal communication going on between teachers, regardless of where they are standing in the room, which goes back to the planning process.” A secondary special educator expressed that “both teachers should be on the same wavelength as far as objectives that need to be met.” Teachers indicated that they do not plan in isolation. Flexible Approach Special educators by and large reported that some teachers view co-teaching roles through a lens of flexibility, while others are more traditional in their view of assigned roles. When discussing different co-teaching approaches, team teaching was identified to be the most common. Teachers conveyed that team teaching allowed teachers to provide remediation and promoted whole-class instruction and progress monitoring. Another teacher reported that she and her co-teacher “Can jump in at times when needed. We both can team-teach the same thing, or I can take a small group and she’ll take the larger group.” Not all co-teaching partnerships have a flexible approach to instruction, however. For example, one special educator reported that she would look to “open the lesson, possibly doing a “Do Now” or opening activity or introducing the task or activity for the day while the general education teacher would take over the content portion.” Teachers at the elementary level noted that their model was different; that is, the special educator was assigned to the class for 90 minutes each day. Accordingly, one teacher said that she pulls students for remediation during the morning math lesson and that the role of the special educator was primarily to break down large chunks of content into manageable pieces.

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General and Special Education Teacher Instructional Practices Table 6 General Education and Special Education Teacher Instructional Practices To what extent are the daily instructional practices consistent with the co-teaching model as defined by Cook and Friend (1995)?

General Education Teacher Themes ● ● ● ● ●

Special Education Teacher Themes ● ● ●

Co-Planning Training Instructional approaches Observations Challenges

Co-planning Flexibility Challenges of co-teaching

General Education Teacher Instructional Practice The second research question explored the extent to which general education teacher instructional practices were consistent with the practices defined by Cook and Friend (1995). Four general education (grades 3 and 4 and two high school) teachers were interviewed. Coplanning, training, instructional approaches, observations, and challenges were discussed during interviews and focus groups. Co-Planning Of the general education teachers interviewed, two worked at an elementary school, and two worked at a high school. It was reported that in all years prior to 2018-2019, the secondary coteachers had a common planning period, except that during the 2018-2019 school year it was scheduled every other day. However, the teachers conveyed that they had worked together for many years and had “a rhythm.” Planning throughout the day was identified as a common practice, with some teachers meeting after the first time they taught the lesson to tweak it for later in the day. Secondary teachers indicated that they planned over the course of several sessions per week. Elementary teachers, conversely, indicated that they had “sit downs” and often collaborated while eating lunch because they only had one planning period per week according to their schedule. They acknowledged daily professional development time that was sometimes unscheduled. The unscheduled time was occasionally used for planning. Elementary and secondary teachers agreed that their co-planning was framed by the question, “What do we want the kids to get out of this?” and then they proceed with meeting students’ unique needs through planning. Training According to the District’s Special Education plan, professional development was provided to all teachers and staff who work with students with disabilities. General education teachers expressed that when the District introduced ICT, they received extensive training (i.e., “When the district 115

first introduced co-teaching, they brought in experts in the field to train us”) but that in recent years, there were fewer trainings. The secondary teachers reported that although the District did not provide “in-house” training, co-teaching partners were often sent to offsite conferences to learn new strategies and plan units and lessons that met the needs of all students. A review of professional development records over the previous 5 years demonstrated that there were several workshops offered that addressed ICT, namely, “ICT Refresher,” “Co-Teaching Instructional Strategies for Mathematics,” “Science Co-Teaching Instructional Strategies,” and “ICT Planning Time.” Co-Teaching Instructional Approaches General education teachers were asked about the frequency with which they utilized particular co-teaching approaches, including “one teach, one assist” (or “one teach, one observe”); parallel teaching; station teaching; alternative teaching; and team teaching (Cook & Friend, 1995). A range of responses was reported. Teachers reported daily use of the “one teach, one assist” approach. When discussing the “one teach, one observe” approach, all participants reported not using it often or “never.” When asked about parallel teaching, some teachers reported using it regularly while others “infrequently.” All teachers expressed that they used the station teaching approach “frequently,” except the elementary third-grade teacher, who said, “We’ll do that later in the year, once we get to the guided reading groups.” All general education teachers, except the high school mathematics teacher, stated that they used the alternative teaching approach “infrequently or never.” The high school math teacher used it “not often.” Finally, teachers reported using the team-teach approach frequently. During an observation of a high school co-teaching mathematics class, students engaged in a station learning activity on the factoring of trinomials. The lesson involved both teachers actively circulating the room and supporting student groups. The special education teacher explained the directions, as well as having the questions written on paper posted around the room. The lesson included “team teaching” and “stations.” Teachers had equal talk time, with neither dominating the discussion. Observation of Integrated Co-Teaching General education teachers were asked what an observer would see when visiting their classroom, and their responses were varied. For example, one said, “It depends on the day but probably parallel teaching.” Teachers conveyed that they were more active in their classes: “I am more of the front and center kind of the leader of the classroom.” Teachers reported that they regularly use technology - specifically, Google Forms. Teachers mentioned that movement in the classroom would be something noticed by an outside observer. Elementary teachers described the use of graphic organizers and color coding and suggested that administrators would see remediation and support during the lesson. Questions were asked about the observation of co-teaching teams. Specifically, it was asked whether the general education and special education co-teachers were invited to the same preobservation and post-observation conference and whether they both received observation writeups at the conclusion of the observation process. Responses were varied. Elementary teachers reported that when they were observed, it was only of one teacher. High school teachers reported that the observations were sometimes of both teachers but mostly of one teacher. 116

Challenges of Co-Teaching Several challenges to effective ICT were identified. Elementary teachers expressed that they had had several students in their classes who were “in the wrong placement,” which “changes the climate in the classroom.” Similarly, secondary teachers reported that students who struggle are often placed in ICT, which made it more difficult to meet the needs of the students who are placed in their class based on their IEPs. Secondary teachers also reported having classes with up to 50% students with disabilities. Elementary teachers spoke at length about the “flaws” in the elementary program. Specifically, they reported that the elementary model of ICT was 90 minutes daily, which meant that for much of the day, there was no special education teacher in the room; however, all ICT classes had a full day special education teaching assistant. Special Education Teacher Instructional Practice Four special education teachers were interviewed to determine whether their practices were consistent with those delineated in the research of Cook and Friend. Teachers were representative of both elementary and secondary, including a third and fourth-grade special education teacher and two high school special educators: one who taught English and one who taught mathematics. Themes included co-planning, training, observations, co-teaching instructional practices, and challenges. Co-Planning Secondary special educators emphasized the importance of daily planning: “It’s every day. It’s always constant.” They described co-planning as flexible: “... co-planning doesn't necessarily have to be that you sat down for a half-hour with your co-teacher.… co-planning can just happen in just several minutes or just the reflection of the first lesson.” Teachers reported that coplanning sometimes occurred by telephone as they drove home. Elementary teachers indicated that the time built into the schedule for co-planning is not enough. One teacher stated, “We kind of discuss things on the fly. Several times, maybe twice per week we’ll talk.” A secondary teacher stated that they plan for the long term over the summer, but they generally plan at least one period together daily. Training During the interviews, special education teachers were asked about the level of training they had in the area of co-teaching. Generally, teachers reported that they had recently participated in district-led professional development. Some teachers reported attending multiple training sessions over the past five years. The participants stated that when co-teaching began, there were training sessions that all co-teachers attended. As co-teaching became more popular, fewer indistrict trainings were offered. Observations Special education teachers were asked about what observers will see when they enter their classrooms. Teachers reported tiered grouping, parallel lessons, and team teaching. Half class/half class (i.e., parallel teaching) allows for half the class to work on review material and the other half to work on station activities. Further, they said that an observer would likely notice, “The engagement of teachers, how they interact with each other.” In a high school 11 thgrade English lesson, an English teacher and her special education co-teacher were observed 117

teaching students how to read and analyze The Oak and the Reed by Jean de La Fontaine. During the lesson, the teachers used two approaches: the “one teach, one assist” approach and the team teaching approach. During the “one teach, one assist” approach, both teachers switched roles. Both teachers therefore had the opportunity to lead the class using that approach. Additionally, when leading the analysis of the reading, both teachers engaged in the team-teaching approach. Co-Teaching Instructional Approaches Special educators reported a range of responses regarding the frequency with which particular co-teaching approaches were utilized in their classrooms. One-teach, one assist was admittedly the most common, where several teachers noted that they used this approach as a regular practice at both the elementary and secondary levels. Parallel teaching, another popular approach, was seldom used. All teachers reported never using the alternative teaching approach and that team teaching was used “sporadically.” During an observation of a third-grade co-teaching team, students were learning about mathematical word problems. At this time, the “one teach, one assist” approach was used exclusively. The general education teacher led the 45-minute lesson, using the SMART Board and circulating the room. The special education teacher remained in the front of the room near the door and contributed to the whole-class instruction with several brief statements such as, “Make sure you write that down,” “OK, everyone, make sure you clear your board,” or “quiet lips, make sure you look at the application problem.” Challenges of Integrated Co-Teaching Special education teachers reported several challenges associated with ICT. Some teachers indicated that they needed to rely on their general education partner for content knowledge. In order to address this, in at least one partnership, planning occurred after school to ensure both teachers had the requisite knowledge to teach the following days’ lessons. In one case, a coteacher explained that despite not being dual certified, she felt confident in her skill based on the number of years she taught a particular English course. She even resorted to using No Fear Shakespeare, a text which presented the play in original language accompanied by a modern English translation so that she was well equipped to instruct students. Moreover, lack of consistency among co-teachers was an area all special educators agreed was problematic. One teacher noted that she had been co-teaching for 10 years and had 10 different co-teachers, which they acknowledged was likely a result of master scheduling, a complex process of scheduling teachers and students to classes based on requirements and electives. One teacher described her long-term partnership as “awesome” due to the “rhythm” between the two teachers. Discussion This study investigated how general education and special education teachers defined the most effective model of ICT. Additionally, this paper explored the extent to which teachers’ actions and practices were consistent with the foundational research of Cook and Friend (1995). This collective case study drew upon the experience of general and special education co-teachers who implemented the ICT model in schools in a suburban school district in New York. The analysis of the findings yields that general education teachers and special education teachers have a range of opinions regarding the implementation of an effective ICT model. The findings related to general education and special education teacher practices were consistent with the practices 118

delineated in the research of Cook and Friend for the secondary teams but not for the elementary teams. General Education Teachers This study found that some general education teacher practices were inconsistent with the recommendations of Cook and Friend. For example, in the Cook and Friend model, “two or more professionals are delivering substantive instruction to a diverse, or blended, group of students in a single space” (Cook & Friend, 1995). During interviews with and corresponding observations of the two secondary co-teaching teams, it was apparent that “substantive” instruction was delivered in a manner that reflected the diverse needs of students. Both teachers were actively engaged in the delivery of instruction and were unified in their approach, evidencing that the requisite planning had been done. In contrast, the elementary observation and interviews with both teams revealed that substantive co-teaching instruction was not consistently delivered due to limited time the elementary special educator was present in the classroom and a lack of coplanning time built into teachers’ schedules. Inclusion of common planning time is essential. As conveyed in Cook and Friend (2010), instructional delivery approaches are meant to utilize the various members of the team to capitalize on their knowledge and understanding of content taught and instructional knowledge related to teaching students with disabilities. During the secondary observations, the teachers utilized multiple approaches, as outlined in Cook and Friend. During the elementary observation, the teachers primarily utilized the “one teach, one assist” approach to deliver a math lesson. Interestingly, the general education teacher at the elementary level reported the frequent use of varied approaches, while the special education counterpart reported that most of the time, the teachers used the “one teach, one assist” approach. Special Education Teachers This study found that elementary special education teacher practices were inconsistent with the recommendations of Cook and Friend. For example, in the Cook and Friend (1995) model, there was a consistent emphasis on planning instruction that meets the IEP goals of students with disabilities. The use of a single primary instructional approach conveys a lack of differentiation and may convey that the educational needs of specific students are not being met. It is incumbent upon teachers but especially the special educator to illuminate students’ IEP goals and keep them at the forefront of all planning. In contrast, the secondary special educators were well-versed in students’ goals and were skillfully able to address those goals during instruction, as evidenced in both observations. Unifying Findings There was a variety of findings related to general education and special education teachers. A unifying finding that related to both general education and special education teachers was the need to minimize the power relationship between teachers in the classroom setting. This finding encompasses the data from both groups related to their effectiveness in developing and maintaining a balanced relationship. Personality conflicts, lack of planning time, and limited special education teacher time in the classroom are real concerns (Daam, Beirnes-Smith & Latham, 2000; Hunter-Johnson, Newton & Cambridge-Johnson, 2014). Differentiation within the classroom setting was used by both groups as a means of reducing tensions related to roles and responsibilities. According to Cook and Friend (1995), “co-teachers develop an array of classroom arrangements for their shared instruction (p. 9). Based on this finding, it appears that the instructional practices of the two secondary co-teaching teams were consistent with Cook and 119

Friend. As Austin (2001) found, a majority of special education and general education teachers agreed that the general education teachers did most in an ICT classroom. In the present study, differentiation was also used to help teachers within each group to identify sources of power within the classroom, as demonstrated when teachers discussed flexible roles. General education teachers and special education teachers viewed co-planning as a power-modifying strategy. Shared planning and respect for the other co-teacher’s role in planning were viewed as positive aspects of the model. Finally, the data were inconsistent between the general education co-teachers and special education co-teachers regarding utilization of the various co-teaching approaches. General education teachers reported that they seldom used the “one teach, one assist” approach, while their special education counterparts reported using this approach frequently. The reported discrepancy may be the result of training and professional development specific to the department. Interestingly, Walther-Thomas et al. (2000) found that the “one teach, one assist” model was effective when the person in the dominant role alternated between the general education teacher and special education teacher. In this study, neither the general education nor the special education teachers indicated that this was part of their practice; however, during secondary observations, this practice was utilized. Bryant Davis, Dieker, Pearl, and Kirkpatrick (2012) found that teachers most frequently used the one teach, one assist approach which establishes the general education teacher as dominant. As emphasized by Friend et al. (1993), a benefit of co-teaching is that unique perspectives and skills of each teacher are brought together to tailor instructional approaches that would not occur if just one teacher were present in the classroom. Another distinctive finding was that in the elementary school, co-teaching was offered in a manner that replicated a push-in service. For example, special education teachers entered the coteaching classroom for 90 minutes per day. By contrast, at the secondary level, ICT classes have a general educator and special educator assigned to each period of the day, as per students’ IEPs. The teachers felt that the elementary ICT model should align with the secondary model with respect to co-planning time and the length of time that both teachers were in the classroom. According to Friend and Barron (2016), co-teachers should spend the majority of their time working with students in a variety of arrangements (including parallel teaching, “one teach, one observe,” station teaching, etc.) Utilizing the six approaches outlined in the research of Friend and Barron increases instructional capacity. Effective implementation means that teachers must have planning time to discuss lessons and co-teaching approaches based on students’ IEPs. Kohler-Evans (2006) surveyed teachers in 15 school districts regarding their experience coteaching. They identified common planning time as the key issue that affected their relationship with their co-teaching partner. In this study, planning time was regularly scheduled at the secondary level. At the elementary level, planning time was scheduled inconsistently. Cook and Friend (1995) state, Ideally, co-teaching includes collaboration in all facets of the educational process. It encompasses collaboratively assessing student strengths and weaknesses, determining appropriate educational goals and outcome indicators, designing intervention strategies and planning for their implementation, evaluating student progress toward the established goals, and evaluating the effectiveness of the co-teaching process (p.4). 120

As reported by teachers, the District’s scheduling of common planning time at the elementary level was inconsistent with the practices outlined in Cook and Friend. Friend et al. (1993) defined co-teaching as a model in which “two or more professionals are delivering substantive instruction to a diverse, or blended, group of students in a single space” (p .1). Second, these the general education teacher and special education teacher share responsibility for a single group of students for content or objectives with equal ownership, shared resources, and accountability (Friend & Barron, 2016). Third, the teachers use all six approaches to address individuals' IEP goals throughout the school year (Friend et al., 2010, p. 12). The present study found that two of the four co-teaching teams skillfully implemented the model as designed. The two secondary teams shared evidence of substantive instruction, shared responsibility, and frequent utilization of the various models. In contrast, the two elementary teams provided some evidence of substantive instruction but did not successfully demonstrate shared instructional responsibilities. For example, during the secondary observations, both teachers were actively engaged in instruction and used various approaches. In contrast, at the elementary level, during the thirdgrade observation, the teachers used the “one teach, one assist” approach throughout the lesson with limited participation by the special education teacher. Admittedly, it was made clear during the interviews that the teachers did not have adequate planning time built into the schedule, and the special educators had responsibilities other than co-teaching due to the nature of the service (i.e., push-in). Recommendations The ICT model effectively meets the needs of general education and special education students in the general education environment. Special education students are exposed to peer role models and there are academic and social benefits for all students. This study presents a snapshot of ICT in a large suburban school district. Additional qualitative and large-scale quantitative studies about the ICT model are needed. While this collective case study addresses a small sample within one large suburban school district, further investigation is needed between and among school districts with varying resources to better understand the dispositions of teachers of all grade levels—in both general education and special education—toward the ICT model. Moreover, additional research should address what data, if any, Districts utilize to evaluate the effectiveness of their ICT programs. Finally, further research studies should evaluate how to bring two divergent perspectives—the general education content perspective and the special education strategy perspective—together to create a rich, robust ICT program in which responsibility is truly shared by both co-teachers. Study Limitations There were several limitations to this qualitative case study. The sample included several general education teachers and special education teachers in two schools in a large suburban school district. Therefore, the sample was not representative of all grade levels and schools. Specifically, not all elementary schools were adequately represented, and the middle school was not included. A study with one school district as its focus makes generalization a challenge; however, the findings of this study may provide school districts with insight into teacher perceptions of ICT and their corresponding instructional practices. Another limitation was that students were not interviewed for this study. Finally, only three of the four co-teaching teams 121

were observed during the data collection process. The fourth elementary team was not observed due to an unforeseen scheduling issue. References Atkinson, P.A. (2015). For ethnography. Thousand Oaks, CA: Sage. Austin, V. L. (2001). Teachers' beliefs about co-teaching. Remedial & Special Education, 22(4), 245. Avramidis, E., & Norwich, B. (2002). Teachers' attitudes towards integration/inclusion: A review of the literature. European Journal of Special Needs Education, 17(2), 129-147. Barbour, Rosaline (2008). Introducing qualitative research: A student guide to the craft of doing qualitative research. London, UK: Sage Publications Ltd Berg, B. L. 1., & Lune, H. (2012). Qualitative research methods for the social sciences (8th ed.). Boston: Pearson. Bryant Davis, K.E., Dieker, L., Pearl, C., & Kirkpatrick, R.M. (2012). Planning in the middle: Co-planning between general and special education. Journal of Educational & Psychological Consultation, 22(3), 208-226. Cook, L., & Friend, M. (1995). Co-teaching: Guidelines for creating effective practices. (Cover story). Focus On Exceptional Children, 28(3), 1. Creswell, J.W., & Poth, C.N. (2017). Qualitative inquiry and research design: Choosing among five approaches. New York, NY: SAGE Publications, Inc. Daam, C.J., Beirne-Smith, M., & Latham, D. (2000). Administrators’ and teachers’ perceptions of the collaborative efforts of inclusion in the elementary grades. Education, 121(2), 331339. Darrow, A. (2016). The Every Student Succeeds Act (ESSA). General Music Today, 30(1), 4144. Friend, M. & Barron, T. (2016). Co-Teaching as a Special Education Service: Is Classroom Collaboration a Sustainable Practice? Friend, M., Cook, L. Reising, M. (1993) Co-teaching: an overview of the past, and glimpse at the present, and considerations for the future. Preventing School Failure, 376-10. Friend, M., Cook, L. Hurley-Chamberlain, D., & Shamberger, C. (2010). Co-teaching: An illustration of the complexity of collaboration in special education. Journal of Educational & Psychological Consultation, 20(2), 9-27. Gately, F. J., & Gately, S. E. (1993). Developing positive co-teaching environments: Meeting the needs of an increasingly diverse student population. Hunter-Johnson, Y., Newton, N. L., & Cambridge-Johnson, J. (2014). What do teachers’ perceptions have to do with inclusive education: A Bahamian context. International Journal of Special Education, 29(1), 143-157. Isherwood, R. S., Barger-Anderson, R., & Erickson, M. (2012). Examining co-teaching through a socio-technical systems lens. Journal of Special Education Apprenticeship, 2(2). Klein, A. (2018, April 20). No Child Left Behind overview: Definitions, requirements, criticisms, and more. Retrieved May 20, 2018, from https://www.edweek.org/ew/section/multimedia/no-child-left-behind-overviewdefinition-summary.html Kohler-Evans, P. A. (2006). Co-teaching: How to make this marriage work in front of the kids. Education, 127, 260–264. 122

Krueger, R. A., & Casey, M. A. (2000). Focus groups: A practical guide for applied research. Thousand Oaks, Calif: Sage Publications Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13(6), 522–525. McCaw, J. S. (2019). Co-teaching: A collective case study of co-teacher Perceptions/Practices and administrator expectations (Order No. 13806260). Available from Dissertations & Theses @ Hofstra University. (2191212782). McMullen, R. C., Shippen, M. E., & Dangel, H. L. (2007). Middle school teachers' expectations of organizational behaviors of students with learning disabilities. Journal of Instructional Psychology, 34(2), 75-80. Merriam, S. B. (1998). Qualitative research and case study applications in education. Revised and Expanded from "Case Study Research in Education". Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook. Thousand Oaks, CA: Sage Publications. Murawski, W. W., & Dieker, L. A. (2004). Tips and strategies for co-teaching at the secondary level. Teaching Exceptional Children, 36(5), 52-58. New York State Education Department. (2019). NYSED Data Site. data.nysed.gov. https://data.nysed.gov/. Scruggs, T. E., Mastropieri, M. A., & McDuffie, K. A. (2007). Co-teaching in inclusive classrooms: A metasynthesis of qualitative research. Exceptional Children, 73(4), 392416. Sileo, J. M. & van Garderen, D. (2010). Creating optimal opportunities to learn mathematics: blending coteaching structures with research-based practices. Teaching Exceptional Children, 42, pp. 14–9. Solis, M., Vaughn, S., Swanson, E. & McCulley, L. (2012) Collaborative models of instruction: the empirical foundations of inclusion and co-teaching. Psychology in the Schools, 49, pp. 498–510. https://d oi.org/10.1002/pits.21606. Taylor, K. R. (2011). Inclusion and the law: Two laws--IDEA and Section 504--Support inclusion in schools. Education Digest: Essential Readings Condensed For Quick Review, 76(9), 48-51. U.S. Department of Education, Office of Special Education Programs, Data Analysis System (DANS), OMB #1820-0517 (2010). ‘‘Part B, Individuals with Disabilities Education Act, Implementation of FAPE Requirements,’’ 2010. Data updated as of August 14, 2012. Walther-Thomas, C., & Bryant, M. (1996). Planning for effective co-teaching. Remedial & Special Education, 17(4), 255. About the Author Jordan McCaw, Ed.D. is the Assistant Superintendent for Pupil Personnel Services in the Massapequa Union Free School District and is an Adjunct Professor in the Educational Leadership Program at Stony Brook University. Dr. McCaw’s research interests include administrative supervision of instructional programs in special education, including the Integrated Co-Teaching model.

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Appendix A Individual Interview Protocol Teachers 1. Tell me about your role within the co-teaching program. 2. For how long have you taught? For how long have you co-taught? 3. For how long have you co-taught with your current co-teaching partner? 4. When did you receive tenure? 5. What grade levels have you taught? 6. As a co-teacher, how would you define the most effective model of co-teaching? 7. Do you believe that your definition is similar to or different from your administrators’ definition? 8. When an observer walks into a co-teaching class, what evidence will he/she see of differentiated instruction, collaboration, and varied instructional approaches? 9. As a co-teacher, what is your role each day? 10. What is your partner’s role? Please discuss: a.

your team’s use of the recommended co-teaching approaches.

b.

how often and to what extent student IEPs and IEP goals are discussed

c.

how often and how extensively you co-plan

d.

if “talk time” during class is balanced or dominated by one teacher

11. How frequently do you utilize the following co-teaching approaches? a. One-teach, one assist b. One-teacher, one observes c. Parallel Teaching d. Station Teaching e. Alternative Teaching f. Team Teaching 12. How often do you co-plan with your co-teacher? 13. What training, if any, have you had in the area of co-teaching? 14. Describe the relationship you have with your co-teacher. 15. What additional supports, if any, have administrators provided you? 16. How effective is co-teaching as a practice for general education students and special education students? 124

Appendix B Focus Group Interview Protocol-General Education Teachers 1. How would you define the most effective co-teaching model? 2. Do you view your relationship with your co-teacher as a partnership? 3. How often do you plan and what does planning look like? 4. In an effective co-teaching model, what role with G.E. teacher have? S.E. teachers? 5. When you are observed in a co-teaching class, do you participate in the post-observation conference? 6. Does your co-teacher provide you with necessary information regarding each of your coteaching student’s disabilities? 7. Does your daily practice resemble the practices associated with the most effective model of co-teaching? 8. How effective is co-teaching as a practice for general education students and for special education students?

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Appendix C Focus Group Interview Protocol-Special Education Teachers 1. How would you define the most effective co-teaching model? 2. Do you view your relationship with your co-teacher as a partnership? 3. How often do you plan and what does planning look like? 4. In the most effective model, what role with G.E. teacher have? S.E. teachers? 5. When you are observed in a co-teaching class, do you participate in the post-observation conference? 6. Does your co-teacher provide you with the necessary resources for your to acquire content knowledge? 7. Does your daily practice resemble your perception of the most effective co-teaching model? 8. How effective is co-teaching as a practice for general education students and for special education students?

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Appendix D Observation Protocol Date: School: Grade Level: Subject: Time: # of Student Present Name of GE teacher Name of SE teacher Evidence Criteria Collaborative Planning -Is there evidence of collaborative planning?

Co-Teaching Approaches -Which co-teaching approaches are used?

Substantive Instruction -Is there evidence of differentiated instruction?

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Using Behavioral Momentum to Increase Compliance in a Preschooler with Autism Beth Pergament, M.S.Ed Mary E. McDonald, Ph.D., BCBA-D, LBA Hofstra University Abstract The current study examined how the use of behavioral momentum, presenting high-probability (high-p) directives immediately prior to low-probability (low-p) directives, increased compliance in a preschooler with autism in a state-approved preschool. The experimental design was an ABAB reversal design. During baseline, low-p directives were presented on a fixed 1-minute interval schedule (FI 1-min). Behavioral momentum was then introduced in the treatment phase by presenting high-p directives followed by low-p directives to increase compliance to the low-p directives. During treatment sessions, high-p directives were presented using a fixed 10-second interval schedule (FI 30-sec). An edible reinforcer was provided upon completion of three consecutive responses to high-p directives. Once compliance to three consecutive high-p directives in a row was obtained, the low-p directive was presented immediately following. Results showed that by using behavioral momentum and presenting 3 high-p directives immediately prior to presenting a low-p directive compliance was increased in the classroom. Using Behavioral Momentum to Increase Compliance in a Preschooler with Autism Student noncompliance can often be an issue for teachers in the school setting (Axelrod, Coolong-Chaffin & Hawkins, 2020). Student noncompliance is something that can be problematic not only for the students, but also for the teachers (Lee et al., 2004). Students who engage in noncompliant behavior are more likely to have difficulty in school (Ray et al., 1999). When categorizing inappropriate behavior, noncompliance appears to be a large issue and can refer to requests, commands, demands, or rules (Ray et al., 1999). When teachers spend a significant amount of time addressing noncompliant behavior, instructional time is lost for the child, the teacher and the classmates. This results in fewer opportunities for social and academic learning and interactions (Belfiore et al., 2008). Noncompliance has been defined as the latency to start a task in a timely manner or the failure to complete a task within a certain time period (Belfiore et al., 2008), failure to initiate or persist at assigned tasks (Lee et al., 2004), and slowness to respond to instructions or complete assigned tasks (Mace, et al., 1988). According to Mace et al., (1988) a high-p sequence involves presenting a series of instruction with a high-p of compliance before presenting an instruction with a lower probability of compliance. Behavioral momentum is the tendency for behavior to maintain following a change in the environmental contingencies (Little & Little-Akin, 2019). Wood et al., (2018) discusses using high probability request sequences as an antecedent intervention to help manage challenging behaviors. They also discuss that the increased rate of reinforcement for compliance creates a momentum of compliant behavior which then carries over when more challenging or less preferred tasks are presented.

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Studies have shown that compliance can be increased by using behavioral momentum in which high rates of reinforcement are provided for high-p directives (demands that are more likely to be complied with) immediately prior to presenting a low-p directive (one that is typically less likely to produce a response or that produces noncompliant refusal behavior) (Romano & Rolf, 2000). If directives that have high-p compliance are used during behavioral momentum techniques, then higher rates of compliance can occur with low-p directives within the same response class (Belfiore et al., 2002). According to Vostal et al. (2011), behavioral momentum has been used successfully to improve reading skills, language arts, and general academic tasks. For example, Leach (2016) showed that the use of the high-p sequence in combination with explicit, systematic and intensive instruction increased teaching multiplication fact fluency in a fourth-grade student. This student did receive special education but was being evaluated to see if he would qualify due to poor academic performance in mathematics. Cameron (2018) demonstrated the efficacy of behavioral momentum on the accuracy of textual and spelling responses in preschool students. In addition, task engagement and compliance during transitions in adolescents with emotional and behavioral disorders have also been addressed successfully through the use behavioral momentum (Vostal, 2011). Behavioral momentum has been shown to be successful with individuals with disabilities and in particular with students with autism (Silla-Zaleski et al., 2010). Rizzo and Belfiore (2015) successfully used behavioral momentum to increase the fluency of tacting (i.e., labeling) responses of preschoolers with autism. Researchers have shown an increase in compliance to directives through the use of behavioral momentum. These can include social interactions such as “give me a hug,” “pick up your toys,” and dressing, such as “put on your sweater” (Ducharme & Worling, 1994) and basic instructions such as “sit down,” “come here,” and “stand up” (Belfiore et al., 2008; Humm et al., 2005). Behavioral momentum has also been used effectively in conjunction with self-video modeling (Halberg, 2018). Two factors can influence the effectiveness of behavioral momentum: the rate at which the antecedent command–response–praise occurs and the delay between the high-p command and the low-p command. The higher the rate of the antecedent high-p command trials, the higher the rate of compliance for the low-p commands (Ray et al., 1999). Zuluaga and Normand (2008) evaluated the effects of the use of reinforcement for high-p requests on the compliance on the low-p requests. It was found that the compliance to the low-p request only increased when the high-p compliance was reinforced. The use of edible reinforcement was shown to increase compliance more so than praise even though both were previously determined to be reinforcing. Ertel et al., (2019) studied the effect of the ratio of high p requests to low-p requests made. The study showed that two of the three participants demonstrated higher rates of compliance when they were exposed to higher rates of high-p requests. Romano and Roll (2000) looked at the use of behavioral momentum to increase compliance in three participants who were age 7, 11, and 20 with diagnoses of autism and intellectual disabilities. They used a compliance assessment to obtain a percentage of compliance by performing 10 trials of each request. During baseline, 10 low-p requests were presented on a FI 129

1-min schedule per session. Each request was presented in a positive tone, and noncompliance was ignored. Social praise was used throughout all phases. During the intervention phase, three to four high-p requests were presented at a FI 30-sec schedule immediately prior to the presentation of one low-p request. They were successful in increasing compliance across participants. The current study aimed to replicate and extend the findings of Romano and Roll (2000) by examining how the use of behavioral momentum could increase compliant behavior in the classroom setting in a preschooler with autism. The current study differed from Romano and Roll (2000) as the participant was of preschool age. In addition, the current study extended Romano and Roll (2000) findings by measuring generalization of compliance to a novel directive during each phase of the study. In addition, edible reinforcers were provided contingent upon correct responding to three consecutive high-p directives based on the protocol used by Zuluaga and Normand (2008). Method Participant The participant in this study was a 3-year-old male preschooler with autism who will be referred to as Jason. The student was living at home with his mother, father, and twin brother. The student was selected to participate in the study due to his inconsistent level of compliance within the classroom setting. Setting The setting was in a self-contained 10:1:2 classroom within a state-approved preschool. There are 10 children, 1 teacher and 2 assistants in this classroom. The student was attending the school five times a week for 5.5 hours per day. The classroom had three rectangular tables each containing three to five chairs at each. There was a desktop computer, a Smart Board, and flat screen TV in the room. The room was set up into several centers. The centers included a block center that had two wooden shelves with blocks and various building toys on it; a dramatic play center with a plastic kitchen set and wooden shelf with dress-up clothes and pretend play materials (i.e., doctor or vet); an art center with a table and wooden shelving unit with various art materials in bins such as watercolors, paintbrushes, crayons, markers, paper, bingo markers, etc.; a sensory center with a table and wooden shelf with various materials such as Play-Doh, Thera Putty, dry pasta, sand, Play-Doh toys, baby shampoo, shaving cream, etc.; a library with a rug and wooden bookshelf with a variety of books; a toys and games center with a table and wooden shelves with a variety of table toys such as linking people, bingo, sorting games, and puzzles, etc.. There were also wooden cubbies along the wall for the children to place their belongings in. Attached to the classroom was a children’s bathroom. There were two doors that can be used to enter the classroom. There was one teacher and two teaching assistants in the classroom. Materials Directives. A list of ten directives was created in order to run the compliance assessment. Table 1 shows the list of directives and conditions needed for each that was used when running the compliance assessment. 130

Table 1 List of Directives Directive

Conditions Needed

1. “Stomp feet.” 2. “Touch nose.” 3. “Give me high five.” 4. “Come here.”

Be within 2 feet of the child. Be within 2 feet of the child. Be within 2 feet of the child. Experimenter needs to be standing 5 feet away from the child. Be within 2 feet of the child. Be within 2 feet of the child. Child needs to have a toy in his hand. Be within 2 feet of the child. Be within 2 feet of the child. Be within 2 feet of the child.

5. “Give me hug.” 6. “Jump.” 7. “Give me _______ (toy).” 8. “Clap hands.” 9. “Do this.” (clap hands) 10. “Do this.” (stomp feet)

After the compliance assessment was performed and it was determined which directives were high-p and which were low-p directives, another list was compiled to break down the conditions needed for each high-p directive and for each low-p directive. Table 2 contains a list of the low-p and high-p directives along with conditions that were used to complete the requests. Table 2 High-p and Low-p Directives. Low-Probability Directives

Conditions Needed for Low-P Directives

High-Probability Directives

1. “Jump.”

Be within 2 feet of child. Be within 2 feet of the child. Be within 2 feet of the child. Be within 2 feet of the child.

1. “Clap hands.”

Conditions Needed for High-P Directives

Be within 2 feet of the child. 2. “Touch nose.” 2. “Stomp feet.” Be within 2 feet of the child. Be within 2 feet of 3. “Do this.” (clap 3. “Give me high the child. hands) five.” 4. “Do this.” (stomp Be within 2 feet of 4. “Give me hug.” the child. feet) 5. “Give me Child needs to Generalization Probe _______(toy).” have a toy in his 1. “Come here.” 5 feet of child hand. _________________________________________________________________________ MotivAider®. A MotivAider® is a device that can be set to various periods of time and will automatically vibrate at either fixed or variable intervals as a reminder to do a task. This was used during the research to ensure that the directives were given at the correct time interval. Response Definitions 131

Noncompliance was defined as the student not completing the directive within 10 seconds of being instructed. Compliance was defined as the student completing the directive within 10 seconds of being instructed. Data Collection Data were collected daily on the percentage of directives followed by the student across baseline and intervention phases. During baseline, data were collected on four separate low-p directives that were given during structured play in the morning and/or play centers. During the intervention phase, data were collected on responding to both the low-p and high-p directives. Data were recorded on the percentage of low-p trials that Jason complied with during intervention sessions. Data were also recorded on his responding to high-p directives. Inter-observer Agreement Inter-observer agreement data (IOA) were collected by a BCBA (Board Certified Behavior Analyst) or teacher assistant on staff at the school. Inter-observer agreement was measured during each phase on the dependent variable. The observers each recorded data on the student’s compliance to low and high-p directives. IOA treatment integrity data were also recorded on the implementation of the independent variable (whether social praise was provided and if the correct directive was used and if it was given in a positive tone). IOA data were calculated by measuring agreements divided by total (agreements plus disagreements) and multiplying by 100 to determine a percentage of agreement. Experimental Design An ABAB reversal design was used in this study. For generalization to be measured, one low-p directive was chosen from the original compliance assessment. During generalization probes, this low-p directive was presented on a FI 1-min schedule two times in a row. Generalization probes were conducted once during each phase of the study. Procedure Compliance assessment A list was compiled of ten simple directives that could be completed within 10 seconds. After the list was compiled, 10 trials of each directive were presented in order to calculate a percentage of compliance for each directive. The compliance assessment was used to verify if the directive fell under the high-p category or the low-p category. The order in which the tasks were presented was varied. It was determined that a low-p directive was a directive that scored less than 70% compliance, and a high-p directive was a directive that scored 70% compliance or higher. The directives were then classified as either high or low-p requests. Of the ten directives assessed, 5 of the directives scored at 70% or above and were used as high-p during the intervention phase of the study. Five of the directives scored below 70% and were categorized as low-p directives. Four of these low-p directives were used throughout the study and the 5 th low-p directive was presented during generalization probes in each phase of the study. Baseline phase During baseline, data were collected on four separate low-p directives that were given during structured play in the morning and/or play centers. Non-compliance to the low-p directive was ignored. A percentage measure was calculated based on the result of the four directives. The 132

directives were presented on a FI 1-min schedule. Each set of four directives comprised one session. All of the directives were presented in a positive tone, and social praise followed regardless of the level of the student’s compliance. Intervention phase During intervention a session was conducted daily. Each session consisted of four trials. A trial consisted of the presentation of three high-p directives followed by one low-p directive. During intervention sessions, the procedures were similar to baseline with the exception that a randomized series of three high-p directives were presented on a FI 30-sec schedule preceding each low-p directive. To ensure high levels of compliance during high-p trials an edible reinforcer was provided after the student complied with the set of high-p directives using a FR-3 schedule. The low-p directive was provided to the student only after he complied with three consecutive high-p directives. If the student did not comply with three consecutive high p directives, the trial would have been terminated as a low-p directive would not be provided. A new trial would then begin with the presentation of three high-p directives. The same four low-p directives were used throughout the experiment to ensure consistency. Noncompliance to the low-p directives was not verbally responded to and new trial was begun. All of the directives were presented in a positive tone, and social praise was provided for appropriate behavior such as good sitting or sharing with peers once per trial and it was not contingent upon the student’s response to the low-probability directive. Return to baseline phase This baseline phase was identical to the first baseline phase. Return to intervention phase This intervention phase was identical to the first intervention phase. Generalization Probes Generalization sessions were conducted once during each phase of the study. During each generalization session two generalization probe trials were conducted. During a generalization probe trial, a novel low-p directive that was not used at any time during the study was presented. The directive was presented two times using a FI 1-min schedule. This measure assessed the student’s ability to respond to a novel low-p directive in the absence of the presentation of high-p directives and edible reinforcers. Social validity In order to measure social validity in the study, a survey was created. The survey was completed by the BCBA supervisor who worked in the school and was assigned to this classroom. The survey measured the satisfaction of the research format both pre- and post- research. The survey was scored on a 7-point Likert-type system. Scores ranged from 1 indicating least/unclear/not at all or not willing and 7 would equal most/very clear/a lot and willing. There were eight questions in total asked both pre- and post- intervention.

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Results Figure 1 shows the percentage of compliance to low-p directives during baseline and treatment phases.

Figure 1. Percentage of Compliance to Low-p Directives during Baseline and Treatment Phases During the initial baseline phase when no high-p directives were present, the student’s compliance to low-p directives was a mean of 60% with a range of 25% to 75%. For the generalization probe during this phase, his compliance level to a novel low-p directive was at a mean of 50%. When the initial treatment phase was implemented and the low-p directive was presented immediately following compliance to three high-p directives), the level of compliance for low-p directives increased to 100% across all sessions. The mean level of compliance during the generalization probe also increased to 100% during the intervention phase. During the return to baseline phase, compliance to low-p directives decreased to a mean of 40% with a range of 25% to 50% and mean compliance during the generalization probe decreased to 0%. During the final return to treatment phase, the compliance to low-p directives increased again to 100% across all sessions. The level of compliance during the generalization probe increased to 100% as well.

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Social Validity Table 3 shows the scores for the pre- and post- intervention questions using a Likert scale 1-7 (1 being least likely and 7 being most likely). Table 3 Social Validity Questions for Pre-Intervention and Post-Intervention

1. How likely is the student to comply with demands throughout the school day? 2. How clear do you feel the instruction are/were for the procedure? 3. How likely are/were you to be able to implement the following procedure? 4. How likely do you think this treatment is going to be in increasing compliance in the student? / How much do you think this treatment increased compliance in the student? 5. How disruptive do you think the treatment will be/ was in the classroom routine? 6. How much do you like the procedure? 7. How willing are you to use these procedures in the classroom in the future? 8. How feasible do you believe these procedures are for the behaviors of the current student? / How feasible do you believe these procedures are for changing the behaviors of future students with similar behaviors?

Pre-Intervention Score

PostIntervention Score

3

5

7

7

6

7

6

6

1

1

7 7

7 7

7

7

The social validity questionnaire assessed a number of areas including level of behavior change (Q1), treatment efficacy (Q4) and treatment acceptability (Q2, Q3, Q5-Q8). In the area of behavior change (Q1) there was a change in rating from 3 to 5 from pre to post survey, therefore it was observed that the level of student compliance increased. In the area of treatment efficacy (Q4) the rating remained at 6 during pre and post survey, this indicates that the rater believed that the intervention would be and was effective during the study. In the area of treatment acceptability (Q2, Q5-Q8) (there was no change pre to post survey, showing that the rater accepted the treatment before and after implementation at the highest level and that the rater did not expect/ did not feel like the treatment would be disruptive with the exception of Q3 in which the rating changed from 6 to 7 indicating ease of implementation. IOA

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Across all phases of the study including both baseline and both treatment phases IOA for the dependent variable of compliance was at 100%. In addition, IOA was also recorded in the form of integrity data on the independent variables (i.e., using a positive tone, use of social praise, use of edible reinforcer) of the study. Inter-observer agreement was 100% on the independent variables of the study across all phases across observers. Discussion This study supported the findings of the Romano and Roll (2000) which showed that compliance can be increased by using behavioral momentum in which responses to high-p directives are reinforced immediately prior to presenting a low-p directive. Jason’s compliance increased after implementation of the intervention and decreased upon the return to baseline condition. During the final intervention phase, Jason’s level of compliance increased again. Although directives were categorized during the compliance assessment into high-p and low-p categories, even within the high-p category there was some variability in student responding. Due to this variability in responding, an edible reinforcer was provided to ensure compliance during the high-p trials. This was done to ensure behavioral momentum would occur. If Jason was not compliant to the high-p directives, there would be no behavioral momentum and therefore, there would be no increase in the compliance to the low-p directives. This study also supported the findings of Zuluaga and Normand (2008) as they found that the compliance to the low-p request only increased when the high-p compliance was reinforced. It is possible that edible reinforcers were required for Jason in the current study due to the limited access that Jason had to reinforcers for compliance prior to the onset of the study. Although the school followed a Positive Behavioral Interventions and Supports (PBIS) model it was observed that the rate of reinforcement used was often at a level that may have been too low for the student. This may have been a contributing factor as to the low level of compliance in the student. This also explains why the behavioral momentum intervention may have been so effective for Jason. The intervention may have also been successful in the classroom due to the ease of implementation (Bross, et al., 2018). This is supported by the social validity data that were collected through the survey. The survey indicated that the behavioral momentum procedure was easy to implement and not disruptive to the classroom. The ease of implementation is important as teachers have many responsibilities in the classroom and are often multi-tasking throughout their day. The survey also indicated that the student was more likely to comply post treatment and that there was a willingness to use these procedures in the future. There were a number of limitations in the current study. One possible limitation of the study was the level of education and expertise of the person completing the social validity survey. The person completing the survey was a Board Certified Behavior Analyst (BCBA) and had many years of experience both designing and implementing behavioral interventions. This may have contributed to the high scores she provided on the pre-test as well as her support of the procedure both pre and post. It is possible that someone who was less versed in the intervention may have scored items differently. 136

Another limitation of this study was that it is unknown if the use of the high-p directives alone (in the absence of reinforcement) would have produced a substantial increase in compliance for Jason. Future research may want to address this issue by conducting a component analysis to determine which component of the intervention had an effect on the behavior. Although the reinforcer was only used during the high-p directives, the student might have been more compliant with the low-p directives expecting a reinforcer to be provided during those trials as well. It would be possible that this reinforcement schedule could have functioned as an intermittent schedule of reinforcement for the student maintaining high rates of responding. It is also possible that responding during high-p and low-p trials could have become differentiated based on provision and absence of a reinforcer. However, over time Jason did not discriminate his responding between high and low-p based on the provision of reinforcement but rather increased responding to 100% to low-p directives during intervention conditions even though a reinforcer was never directly provided to responding during low-p trials. It appears that it was the momentum of responding that allowed for Jason to respond to the low-p directives. It is important to note that Jason’s parents reported increases in compliance and a decrease in challenging behavior at home while this study was taking place. Related service providers and classroom teacher assistants also reported a positive change in Jason’s behavior during the study. There was also interest from other classroom personnel in learning about the intervention to use with students in their respective classrooms. In future research, it would be important to assess compliance with multiple staff members to look for generalized responding across instructors. It is possible that a history of reinforcement with a particular instructor may also influence student responding and would need to be accounted for. It would be advisable for future researchers to analyze the use of reinforcers for responses to the high-p directives. It is recommended that the student’s individual needs are taken into account when developing a behavioral momentum protocol. Components of the methodology such as selection of high and low-p directives, rate of high-p directives, use of reinforcers, use of reinforcers for responding to high-p directives, the use of edible reinforcers and the use of social praise are all areas of consideration for an individual student. References Axelrod, M. I., Coolong-Chaffin, M., & Hawkins, R. O. (2020). Behavioral momentum. SchoolBased Behavioral Intervention Case Studies: Effective Problem Solving for School Psychologists, 111. Belfiore, P. J., Basile, S. P., & Lee, D. L. (2008). Using a high-probability command sequence to increase classroom compliance: The role of behavioral momentum. Journal of Behavioral Education, 17(2), 160–171. Belfiore, P. J., Lee, D. L., Scheeler, C., & Klein, D. (2002). Implications of behavioral momentum and academic achievement for students with behavior disorders: Theory, application, and practice. Psychology in the Schools, 39(2), 171–179. Bross, L. A., Common, E. A., Oakes, W. P., Lane, K. L., Menzies, H. M., & Ennis, R. P. (2018). High-probability request sequence: An effective, efficient low-intensity strategy to support student success. Beyond Behavior, 27(3), 140-145. 137

Cameron, K. L. (2018). The Effects of a Behavioral Momentum Blending Intervention on the Accuracy of Textual and Spelling Responses Emitted by Preschool Students with Blending Difficulties. Columbia University. Ducharme, J. M., & Worling, D. E. (1994). Behavioral momentum and stimulus fading in the acquisition and maintenance of child compliance in the home. Journal of Applied Behavior Analysis, 27(4), 639–647. Ertel, H., Wilder, D. A., Hodges, A., & Hurtado, L. (2019). The effect of various highprobability to low-probability instruction ratios during the use of the high-probability instructional sequence. Behavior Modification, 43(5), 639655. Halberg, I. A. (2018). Video Self-Modeling as an Intervention to Address Noncompliance in Preschoolers (Doctoral dissertation, Indiana University). Humm, S. P., Blampied, N. M., & Liberty, K. A. (2005). Effects of parent-administered, homebased, high-probability request sequences on compliance by children with developmental disabilities. Child & Family Behavior Therapy, 27(3), 27–45. Leach, D. (2016). Using high-probability instructional sequence and explicit instruction to teach multiplication facts. Intervention in School and Clinic, 52(2), 102-107. Lee, D. L., Belfiore, P. J., Scheeler, M. C., Hua, Y., & Smith, R. (2004). Behavioral momentum in academics: Using embedded high-p sequences to increase academic productivity. Psychology in the Schools, 41(7), 789–801. Little, S. G., & Akin-Little, A. (2019). Reductive procedures: Positive approaches to reducing the incidence of problem behavior. In Behavioral interventions in schools: Evidencebased positive strategies, 2nd ed. (pp. 77-96). American Psychological Association. Mace, F. C., Hock, M. L., Lalli, J. S., West, B. J., Belfiore, P., Pinter, E., & Brown, D. K. (1988). Behavioral momentum in the treatment of noncompliance. Journal of Applied Behavior Analysis, 21(2), 123–141. Ray, K. P., Skinner, C. H., & Watson, T. S. (1999). Transferring stimulus control via momentum to increase compliance in a student with autism: A demonstration of collaborative consultation. School Psychology Review, 28(4), 622–628. Rizzo, K., & Belfiore, P. J. (2015). A behavioral momentum approach is effective for prompting acquisition and fluency outcomes of tacting in children with autism spectrum disorder. Evidence-Based Communication Assessment and Intervention, 9(2), 66-70. Romano, J. P., & Roll, D. (2000). Expanding the utility of behavioral momentum for youth with developmental disabilities. Behavioral Interventions, 15(2), 99–111. Silla-Zaleski, V. A., & Vesloski, M. J. (2010). Using DRO, behavioral momentum, and selfregulation to reduce scripting by an adolescent with autism. The Journal of Speech and Language Pathology -Applied Behavior Analysis, 5(1), 80–87. Vostal, B. R., & Lee, D. L. (2011). Behavioral momentum during a continuous reading task: An exploratory study. Journal of Behavioral Education, 20(3), 163–181. Wood, C. L., Kisinger, K. W., Brosh, C. R., Fisher, L. B., & Muharib, R. (2018). Stopping behavior before it starts: Antecedent interventions for challenging behavior. TEACHING Exceptional Children, 50(6), 356–363. Zuluaga, C. A., & Normand, M. P. (2008). An Evaluation of the high-probability instruction sequence with and without programmed reinforcement for compliance with high-probability instructions. Journal of Applied Behavior Analysis, 41(3), 453–457.

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About the Authors Beth Pergament MS. Ed: Beth Pergament is a Special Education Itinerant Teacher. She has worked in the field of special education and with students with disabilities as a special educator for 13 years. Beth has worked with preschool students with various disabilities such as autism and learning disabilities in both home and school settings. Beth also works with children in early intervention in the home-based setting. She has completed her Master’s degree in Early Childhood Special Education as well as her Advanced Certificate in Applied Behavior Analysis. Mary E. McDonald, Ph.D, BCBA-D, LBA: Dr. Mary E. McDonald is a Professor in the Department of Specialized Programs in Education at Hofstra University in Hempstead, NY. She is currently the Program Director for the Advanced Certificate Programs including the Advanced Certificate in Applied Behavior Analysis Program. She is a Board Certified Behavior Analystdoctoral level and a licensed behavior analyst in the states of NY and CT. Dr. McDonald has worked in the field for over 30 years and currently directs programs for students with autism at Eden II’s Genesis Programs. She has published a book on including students with ASD as well as chapters on technology and evidence-based interventions. She has published peer-reviewed and popular articles on topics such as: self-management, social reciprocity, PECS, scripts and semantic mapping, creativity and Universal Design for Learning.

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A Model for Conducting a Brief Experimental Analysis to Assess a Student’s Ability to Perform a Replacement Behavior Linda M. Reeves, Ph.D Abigail Baxter, Ph. D University of South Alabama Abstract This article describes a practical and promising method to use when designing function-based behavioral interventions: assessing a student’s ability to perform a replacement behavior independently. The method combines the use of task analysis and a brief experimental analysis to aid in the selection of effective intervention components. The systematic method provides a direct and data-driven approach to determine the need to include instructional strategies to explicitly teach the replacement behavior in an intervention. Two case studies illustrating the use of the method are provided. A Model for Conducting a Brief Experimental Analysis to Assess a Student’s Ability to Perform a Replacement Behavior Function-based interventions are individualized, multi-component behavior plans that are linked to the results of a functional behavioral assessment (FBA) of challenging behavior. The purpose of the FBA is to gather information about: (a) the antecedent conditions that set the occasion for a clearly defined target behavior, and (b) the consequences that maintain the target behavior (Johnston et al., 2020). The function, or purpose, of the problem behavior is then hypothesized to develop a function-based intervention to directly address the reason(s) why the problem behavior occurs. Behavioral interventions based on the results of an FBA have been found to be more effective than interventions not based on FBA information (Koegel et al., 2012). When designing function-based interventions, it is necessary to define and increase a replacement behavior that serves as an alternative to the problem behavior (Horner et al., 2005). The replacement behavior is a prosocial behavior we want a student to exhibit instead of the problem behavior. Generally, replacement behaviors serve the same function(s) as inappropriate behavior by allowing an individual to meet a need in a more socially acceptable way (McKenna et al., 2017). The selection of a replacement behavior is based on the results of a FBA. For example, if the function of a student’s throwing behavior in a classroom is to avoid an assignment, the student is encouraged or taught to ask for a break as a functionally-equivalent alternative response that serves the same purpose of avoiding the assignment. FBA data may also be used to identify replacement behaviors that are incompatible with problem behaviors (Gresham, 2015). For example, if a student leaves their seat to access teacher attention, then raising their hand to access teacher attention while remaining in their seat could be selected as a replacement behavior. Ultimately, the occurrence of a replacement behavior should result in reinforcement faster, easier, and more reliably than the problem behavior (Roberts, 2017). A student’s ability to perform the replacement behavior independently (i.e., fluently enough to be reinforced naturally) plays a key role in intervention design. Interventionists need to determine whether a student’s failure to perform a replacement behavior fluently reflects a performance 140

deficit or a skill deficit. Students with a performance deficit fail to perform the replacement behavior at acceptable levels even though they have the knowledge and ability to perform the skill. In contrast, students with a skill deficit lack the knowledge and/or ability to perform the replacement behavior, even under optimal conditions (Gresham et al., 2006). There is a need, therefore, to identify effective, direct, and data-based practices to assist interventionists in making that determination. The need to distinguish between a skill deficit and a performance deficit was first made by Bandura (1969, 1977) in regards to his social learning theory. Gresham (1981) also made this delineation in regards to teaching social skills to individuals with disabilities. Gresham (1981) explained that the type of deficit, skill or performance, would result in the selection of different intervention methods. In a review of social skills training studies 25 years later, however, Gresham et al. (2006) concluded that most of those studies failed to assess whether the individuals had the skills necessary to perform the social skills chosen. Unfortunately, this continues to be an area of weakness in the literature. The Function-Based Intervention Decision Model (Umbreit et al., 2007), an evidence-based approach to conducting FBAs and designing inventions, assists users in selecting intervention methods by answering two questions. The first question asks whether the individual can perform the replacement behavior. If the answer to this question is yes, then the intervention will consist mainly of adjusting environmental contingencies to prevent the problem behavior and providing reinforcement for the replacement behavior instead of the problem behavior. However, if the answer to the first question is no, then the intervention must include instruction to teach the student the skills (e.g., social, adaptive, communication, academic) needed to perform the replacement behavior fluently. Other than the information obtained during the FBA, however, no systematic assessment process to assist users to make that determination is described. Traditionally, FBA does not include a systematic assessment process to help interventionists answer these questions, which may lead to unsuccessful interventions. Replacement Behavior Training (RBT; Maag, 2005) is also based on FBA data. The goal of RBT is to teach individuals functionally equivalent behaviors. Maag (2005) suggested that the assessment of the replacement behavior should begin by ruling out a behavioral skill deficit. The RBT approach consists of identifying a replacement behavior based on the FBA results, then implementing an intervention where the student is reinforced contingent on exhibiting the replacement behavior. If the student successfully performs the targeted replacement behavior, then it is assumed the person has the skills in their repertoire. If the student does not perform the replacement behavior under the reinforcement condition, a skill deficit is then hypothesized. Although researchers describe the need to replace problem behaviors with socially appropriate behaviors (Dunlap et al., 2010; McKenna et al., 2017; Moreno & Bullock, 2011), little direction or details about the method or data used to assess a student’s ability to perform a replacement behavior prior to designing an intervention has been provided in the literature. Furthermore, in a review of research on social skills training, Maag (2005) found that a problem with the training techniques used was that they were rarely linked to the reason why individuals initially failed to perform appropriate social skills (i.e., lack prerequisite behavioral skills, interpret social cues incorrectly). The design of function-based interventions, using information about skill and/or performance deficits, should lead to more effective learning. 141

In a review of FBA literature, three methods have been described to assess whether a student can perform the replacement behavior prior to intervention design. The most common description is a brief statement that observation methods were used; however, no further details are provided, such as the behaviors that were observed or the observation methods used. A second method used to distinguish between skill and performance deficits is the Social Skills Rating System (SSRS; Gresham & Elliott, 1990) or the revised SSRS – the Social Skills Improvement System—Rating Scales (SSIS-RS; Gresham & Elliott, 2008). Although this indirect method relies on the use of data to inform decisions, it is not necessarily individualized nor directly linked to an individual’s FBA results. A third method involves a task analysis of the replacement behavior (Reeves et al., 2013) combined with a brief experimental analysis (Reeves et al., 2017) to assess a student’s ability to perform a replacement behavior prior to designing the intervention. The purpose of this article is to illustrate the use of this last method described. The task analysis combined with brief experimental analysis offers a practical and empirical approach to assess a student’s ability to perform a replacement behavior in order to assist with the selection of appropriate intervention components. Procedure After completing a FBA to hypothesize the function of a problem behavior and selecting a functionally-equivalent replacement behavior, the student’s ability to perform the replacement behavior fluently should be assessed to determine whether the intervention needs to include strategies to teach the replacement behavior directly or should only include strategies to make the replacement behavior more likely to occur. Generally, if a student is observed consistently performing the replacement behavior independently either in the same context or another setting, it is likely that the student already has the skills. At other times, however, interview and observation data alone may not be sufficient to determine whether a student can already perform a replacement behavior. The method illustrated in this article allows interventionist to directly assess a student’s ability to perform a replacement behavior fluently to determine whether the intervention needs to include instructional components to teach the student the replacement behavior or not. In general, if a student fails to perform a replacement behavior at acceptable levels in the natural environment, their ability to perform the replacement behavior should be assessed to pinpoint the exact substep(s) within the task analysis that requires direct instruction to perform independently. Assessing an individual’s ability to perform a replacement behavior involves a few easy steps. First, a task analysis of the steps involved in completing the replacement behavior is developed. Data are then collected on the students’ performance for each step in the task analysis. Next, a reinforcement contingency is introduced briefly to test whether the student will perform more steps in the replacement behavior under highly motivating conditions. If the student performs each step in the task analysis independently under this brief experimental analysis (BEA) condition, then those results are used to confirm a performance deficit. However, if a student’s performance does not change to acceptable levels under the BEA condition, then those results are used to confirm a skill deficit. To illustrate the use of the method, two case studies of students diagnosed with autism spectrum disorder (ASD) are presented. A description of each student is provided followed by an example of how the method was implemented. 142

Case Study 1: Davis Davis was a 5-year-old diagnosed with ASD. He attended kindergarten class with 27 students, his teacher, and a teacher’s assistant. Although academically Davis functioned above grade level, he also exhibited problem behaviors that disrupted the class. Davis’ off-task behaviors included engaging in activities other than the assigned task for 10 s or more, playing with materials, taking items from peers, crying, screaming, demanding items, and interrupting the teacher. In addition to interviewing Davis’ teacher and classroom assistant, direct observations occurred during language arts and math centers for a total of 3 hours over 4 days. The FBA results revealed that Davis engaged in the problem behavior to get teacher attention, assistance, and information on what he was supposed to do. Based on these results, Davis’ on-task replacement behavior was defined as completing the steps necessary to engage in activities independently, requesting items from peers, and getting his teacher’s attention by approaching and waiting to be acknowledged. Case Study 2: Calli Calli was a 12-year-old sixth grader diagnosed with ASD and speech/language impairment. She was placed in a self-contained class, but attended resource class for language arts with 16 other students and an assistant who accompanied her to class. Calli’s off-task behaviors included engaging in activities other than the assigned task, making rude comments or speaking rudely to teachers and peers (e.g., saying “I hate you”), complaining, raising her voice, screaming, whining or crying, banging on her desk, falling to the floor, and hitting her head. In addition to conducting structured interviews with Calli’s teachers and assistant, direct observational data were collected during five sessions for a total of 3.5 hours in Calli’s language arts class. The FBA results hypothesized that Calli engaged in off-task behavior when (a) the teacher gave her instructions, (b) she was asked to share information, and (c) she was called on to participate to get attention from her teachers and peers (e.g., additional explanations, reprimands, prompts to ask for help or a break, peers looking or laughing at her) and to avoid the assignments and expectations. Based on the FBA results, Calli’s on-task replacement behaviors were defined as completing class assignments quietly, raising a hand to ask a question related to the topic or to ask for help, saying “no thank-you” when asked to participate, and “excuse me” to get someone’s attention. Step 1: Task Analyze the Replacement Behavior The first step to assess a student’s ability to perform the replacement behavior independently is to task analyze, or break down, the steps involved in completing the replacement behavior into measurable and observable discrete behaviors in the sequence in which they occur. “Task analysis serves a useful diagnostic function by helping teachers pinpoint their students’ specific functioning levels on targeted skills” (Moyer & Dardig, 1978, p. 16). Task analysis is also commonly used to systematically teach behaviors by gradually chaining the individual steps to form a complex behavior (Alberto & Troutman, 2013). Each step in the task analysis serves as the discriminative stimulus for the next step. Systematic prompting and feedback are used to teach students to perform each step in a task analysis independently. Data are collected on the students’ performance to monitor their progress and make instructional decisions such as when to add the next step in the chain, or break a current step into even more discrete behaviors. The 143

use of task analysis for instruction has a solid evidence base for teaching daily living skills (Bouck, 2010); self-care-skills (Cobb & Alwell, 2009) and academic content (Courtade et al., 2010) for students with moderate to severe disabilities and students with ASD (Sam et al., 2020). Task analyses are individualized for each student given their current skill levels, and therefore; the number of steps for each task analysis will differ depending on each individual. A task analysis can be developed using one of four methods. First, the sequence of steps is developed after observing someone else perform the desired behavior. A second method is to consult persons skilled in exhibiting the behavior. The third method is for the person developing the task analysis to document the steps involved while performing the behavior themselves. The final method is to develop an initial task analysis and then refine and revise it as needed (Cooper et al., 2020). There are no rules for determining the correct number of steps in a task analysis, and practitioners must also be prepared to adjust the sequence of steps in the task analysis when needed. The following are two examples of task analyses of replacement behaviors. Table 1 Case Study 1: Task Analysis of Davis’ Replacement Behaviors 1. Task engagement: a) Joins assigned table within 1 minute of teacher prompt to begin b) Gets material to complete the assignment c) Writes name on assignment d) Works on the steps necessary to complete the assignment e) Puts away materials when the assignment is complete or when told to do so f) Remains at the table or interacts with teacher appropriately 2. a) b) c)

Requests items from peer appropriately: Get peer’s attention by using a calm voice tone to call name or approaching Makes request (using an appropriate voice tone) Waits quietly and calmly for a turn, response, or item

3. a) b) c)

Get teacher’s attention appropriately: Raises hand or approaches teacher Says, “excuse me” one time (using an appropriate voice tone) Waits quietly until teacher gives attention

Table 2 Case Study 2: Task Analysis of Calli’s Replacement Behaviors 1. a) b) c) d) e)

Engages in assignment: Gets materials when necessary (book from shelf, paper, pencil) Starts assignment within 30 seconds of teacher request Engages in the assignment Sits quietly Puts materials away when assignment is complete or when told to do so 144

2. a) b) c)

Raises hand to ask a question or ask for help: Raises hand Waits to be called on Asks an appropriate question

3. Says “no thank you” when called on: a) Uses appropriate voice tone b) Says, “no thank you” 4. a) b) c) d) e)

Says “excuse me” to get someone’s attention: Looks in person’s vicinity Uses appropriate voice tone Says, “excuse me Waits for person to acknowledge Makes an appropriate comment

Step 2: Collect Baseline Data Once the replacement behavior has been task analyzed, the next step in assessing a student’s ability to perform the replacement behavior fluently is to collect baseline data to determine the individual steps in the task analysis the student can perform independently and those steps where additional prompting is needed. As the student completes the behavioral chain during naturally occurring classroom activities, record an “I” for independent, “V” for verbal prompt, “G” for gesture prompt, or “P” for physical prompt for each step in the chain based on the student’s performance. For the case studies illustrated in this article, baseline data were collected during three separate observations to obtain an average performance level. If needed, however, additional data may be collected to establish a consistent pattern of responding. For the purposes of summarizing the data, the students’ performance in the case studies are described in terms of the overall percentage of steps performed independently in the task analysis. While this serves to provide a general description of the student’s performance, evaluating a student’s performance across each step in the task analysis would allow a better comparison of each discrete behavior in the chain across baseline and the BEA condition. Case Study 1: Davis Davis’ ability to perform the replacement behaviors independently was assessed for a total of 4 days during math center activities. Data were collected on whether he completed each step in the task analysis of his replacement behaviors: (a) task engagement, (b) requesting items from peers appropriately and, (c) getting his teacher’s attention appropriately (see Figure 1). For the first 3 days, data indicated that Davis independently performed the steps for engagement an average of 12% (range 0-20%), and the steps for both requesting items from peers and getting the teacher’s attention 0% of the time under typical classroom conditions.

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Case Study 2: Calli Calli’s ability to perform the replacement behaviors independently was assessed over a total of six days during her language arts class. Data were collected on whether Calli performed each step in the task analysis for the replacement behaviors: (a) task engagement, (b) raise hand to ask for help or an appropriate question, (c) say, “no thanks,” when asked to participate and, (d) “excuse me,” to get someone’s attention (See Figures 2–5). Because task engagement included engaging in the assignment and sitting quietly, data were collected using 30 second whole intervals for at least 25 minutes. Data on the remaining social behaviors were collected using frequency data-based on the number of opportunities during class. For the first three days, the task analysis assessment showed Calli performed the steps in task engagement an average of 14% (range 0-42%) of the observed intervals, and performed the steps in raising hand, saying “no thanks,” when called on, or “excuse me,” to get someone’s attention 0% of the time. Step 3: Arrange a brief experimental analysis The third step to assess a student’s ability to perform a replacement behavior is to arrange a reinforcement contingency to briefly test whether the student completes more steps in the task analysis of the replacement behavior under highly motivating conditions. This requires the selection of a highly preferred reinforcer which must also be individualized for a student based on their interests and preferences. As Daly et al. (1997) point out, if a student fails to respond to incentives, then either the student does not have the skills or the wrong incentives were used. A similar approach of using incentives to identify a skill or performance deficit has been described in regards to assessing students’ academic skills (Daly et al., 1998; Reisener et al., 2016). A BEA has been used to identify interventions for reading fluency (Burns et al, 2017), mathematics (McKevett et al., 2021), and writing (Parker et al, 2012). To select effective interventions, a brief contingent reinforcement condition is arranged to rule out the possibility of a performance deficit. Under the contingent reinforcement condition, the students are offered incentives for improved performance. For example, Daly et al. 1997 offered a student, who read 87 words per minute under typical classroom conditions, a preselected reward if she could read 100 words correctly per minute. Under the contingent reinforcement condition, the student read only 72 words correctly per min, indicating that her low oral reading fluency rate was a result of a skill deficit. This information was then used to provide direct instruction to improve the student’s oral reading fluency. In contrast, if a student meets the criteria under the contingent reinforcement condition, a performance deficit is identified, and therefore; the intervention consists of adjusting contingencies by providing reinforcement systematically for increased rates of accuracy and speed. The use of BEA to assess a student’s academic performance has a solid research base (Burns et al., 2017). This same method was applied to assess a student’s ability to perform a replacement behavior to determine a performance or skill deficit. Case Study 1: Davis Davis’ teacher selected a reinforcer that he frequently requested and she identified as something he was highly motivated to earn. Davis was told he could earn his choice of a picture of a solar system or a map if he did his work, raised his hand when he wanted to tell the teacher something, and use his words to ask for items at centers. 146

Case Study 2: Calli The reinforcers for Calli were identified by herself and her teachers as highly preferred activities. Calli was told that, if she did her work and was respectful to her teachers, she could leave class 15 min. early to either listen to her favorite music CD or use the computer. Step 4: Collect task analysis data and analyze to determine intervention components. The final step to assess the replacement behavior is to collect data on each step in the task analysis under the BEA condition to compare to the student’s baseline performance. If a student fails to perform the replacement behavior independently during the BEA condition, then a skill deficit is determined. Therefore, intervention components need to include strategies to directly teach the replacement behavior. Case Study 1: Davis When Davis was offered a highly preferred incentive to perform the replacement behavior independently under the BEA condition, he continued to independently perform only 20% of the steps for engagement and 0% for requesting items from peers and getting teacher’s attention appropriately. Given his continued low performance under the reinforcement condition, a skill deficit was determined across all three replacement behaviors (Figure 1).

Percentage of Steps Completed Independently

Davis 100

Baseline Reinforcement Contingency

80 60 40 20 20

20

17 0

0 1

2

3

4

Days Task Engagement

Social

Figure 1. Percentage of steps of the replacement behavior Davis completed independently.

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Davis’ intervention components were directly linked to the FBA and BEA results. In addition to being reminded of the behavioral expectations before math class and receiving reinforcement every 5 min he remained on task, Davis was taught how to use visual instructions of the steps needed to complete his class activities. Davis also practiced examples and non-examples of getting his teacher’s attention and requesting items from peers. Davis’ teachers were also taught to use a hand signal to let him know how long he needed to wait before getting their attention. This intervention resulted in an increase in Davis’ on-task behavior. Case Study 2: Calli

Percentage of Engaged Intervals

Under the reinforcement contingency, Calli performed the steps in task engagement an average of 86% (range 67-98%) of the intervals (Figure 2), and said “no thanks,” 100% of the time based on one opportunity (Figure 3), but she performed the steps to raise her hand 0% of the time (Figure 4), and get someone’s attention appropriately only 63% (range 59-66) of opportunities (Figure 5). These data confirmed a performance deficit with remaining engaged and saying, “no thanks,” but a skill deficit with raising hand and getting someone’s attention.

Calli Task Engagement 92

100

Baseline

80

98

67

Reinforcement Contingency

60 42 40 20 0

0

1

2

0 3

4

Days

Figure 2. Percentage of engaged intervals for Calli.

148

5

6

Percentage of Steps Performed Independently

100

100

Calli "No Thanks"

80 60

Reinforcement Contingency

Baseline

40 20 0

1

2

3

4

5

6

Days

Percentage of Steps Performed Independently

Figure 3. Percentage of steps Calli performed independently for saying, “no thanks,” when asked to participate.

Calli Hand Raising

100 80

Baseline

Reinforcement Contingency

60 40 20 0

1

2

3

Days

4

5

6

Figure 4. Percentage of steps Calli performed independently for raising her hand to ask for help or an appropriate question.

149

Percentage of Steps Performed Independently

100

Calli "Excuse Me"

80 66

Baseline

60

63 59

Reinforcment Contingency

40 20 0

1

2

3

4

5

6

Days Figure 5. Percentage of steps Calli performed independently for saying, “excuse me” to get someone’s attention. Based on the results of the FBA and BEA, Calli’s intervention included not only antecedent adjustments, to prevent the target behavior while making the replacement behavior more likely to occur, and a reinforcement procedure for the occurrence of the replacement behavior, but also direct instruction on how to ask for help and get someone’s attention. She was taught to raise her hand to get the teacher’s attention, and to get the assistant’s attention by looking in her vicinity and saying “excuse me.” Explicit instruction also included having Calli draw appropriate and inappropriate examples of getting someone’s attention, including what others would think or respond based on each response. A list of appropriate and inappropriate comments was also developed. A visual reminder of appropriate attention getting comments was placed in Calli’s vicinity to serve as a prompt of the expected behavior. As a result of the intervention, Calli’s ontask behavior increased. Conclusion Assessing not only the function of a problem behavior, but also a student’s ability to perform a replacement behavior independently is vital to designing appropriate and effective behavioral interventions. As function-based replacement behavior interventions become more common to address students’ social skills deficits (Gresham, 2015; McKenna et al., 2016), data-based methods for determining intervention components are useful. As Burns et al. (2017) undoubtedly points out regarding the use of BEA, “using a theoretical or conceptual framework would also reduce the likely influence of error because it would be a systematic testing rather than throwing multiple condition into one examination to determine with one is best” (p. 652). A successful function-based intervention is developed using the results of the FBA. Once a replacement behavior has been identified, the task analysis of the replacement behavior and BEA offer a versatile way to determine whether the intervention needs to include strategies to explicitly teach a student the skills needed to perform the replacement behavior, or only contingencies need to be adjusted to make it more likely the replacement behavior will occur. In 150

addition, the task analysis data also allows for the identification of the specific subskills (steps) within a complex behavioral sequence that requires direct and systematic instruction for a student to perform independently. Although the combination of task analysis and BEA offer a promising way to assess a student’s ability to perform a replacement behavior, additional research is needed to ensure its effectiveness when designing successful function-based interventions. Future research should be conducted using this method with a variety of students who vary in age and disability. In particular, future research to determine whether a minimum number of stable data points are needed under each BEA condition (baseline, reinforcement contingency) and whether a particular criterion (i.e., 80%) would indicate acceptable levels of performance under the BEA condition is needed. The results of such work are essential to clarify a systematic process to aid in the identification and selection of effective intervention components. References Alberto, P., & Troutman, A. C. (2013). Applied behavior analysis for teachers. Pearson. Bandura, A. (1969). Principles of behavior modification. Holt, Rinehart, & Winston. Bandura, A. (1977). A social learning theory. Prentice-Hall. Bouck, E. C. (2010). Reports of life skills training for students with intellectual disabilities in and out of school. Journal of Intellectual Disability Research, 54(12), 1093-1103. https://doi.org/10.1111/j.1365-2788.2010.01339.x Burns, M. K., Taylor, C. N., Warmbold‐Brann, K. L., Preast, J. L., Hosp, J. L., & Ford, J. W. (2017). Empirical synthesis of the effect of standard error of measurement on decisions made within brief experimental analyses of reading fluency. Psychology in the Schools, 54(6), 640-654. https://doi.org/10.1002/pits.22022 Cobb, R. B., & Alwell, M. (2009). Transition planning/coordinating interventions for youth with disabilities: A systematic review. Career Development for Exceptional Individuals, 32(2), 70-81. https://doi.org/10.1177/0885728809336655 Cooper, J.O., Heron, T.E, & Heward, W.L. (2020). Applied behavior analysis (3rd Edition). Pearson Education, Inc. Courtade, G., Browder, D. M., Spooner, F. H., & DiBiase, W. (2010). Training teachers to use an inquiry-based task analysis to teach science to students with moderate and severe disabilities. Education and Training in Developmental Disabilities, 45(3), 378-399. Daly, E. J., Martens, B. K., Dool, E. J., & Hintze, J. M. (1998). Using brief functional analysis to select interventions for oral reading. Journal of Behavioral Education, 8(2), 203-218. Daly, E. J., Witt, J. C., Martens, B. K., & Dool, E. J. (1997). A model for conducting a functional analysis of academic performance problems. School Psychology Review, 26(4), 554–574. https://doi.org/10.1080/02796015.1997.12085886 Dunlap, G., Iovannone, R., Kincaid, D., Wilson, K., Christiansen, K., Strain, P., & English, C. (2010). Prevent-teach-reinforce: The school-based model of individualized positive behavior support. Brookes. Gresham, F. M. (1981). Social skills training with handicapped children: A review. Review of Educational Research, 51(1), 139–176. https:/doi.org/ 10.2307/1170253 Gresham, F. (2015). Evidence‐based social skills interventions for students at risk for EBD. Remedial and Special Education, 36(2), 100–104. https://doi.org/10.1177/0741932514556183 151

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http://www.socialinnovationsjournal.org/editions/issue-39/75-disruptiveinnovations/2615-the-competing-behavior-pathway-model-developing-function-basedsupports-for-students-with-problem-behavior Sam, A. M., Cox, A. W., Savage, M. N., Waters, V., & Odom, S. L. (2020). Disseminating information on evidence-based practices for children and youth with autism spectrum disorder: AFIRM. Journal of Autism and Developmental Disorders, 50(6), 1931– 1940. https://doi.org/10.1007/s10803-019-03945-x Umbreit, J., Ferro, J. B., Liaupsin, C. J., & Lane, K. L. (2007). Functional behavioral assessment and function-based interventions: An effective, practical approach. PrenticeHall. About the Authors Linda M. Reeves, Ph. D., is an assistant professor of Special Education for the College of Education and Professional Studies at the University of South Alabama (USA). Before earning her Ph.D., Linda worked in the field of special education for over 25 years as a special education teacher, autism specialist, and a district behavior specialist. She also serves as a Co-PI for PASSAGE USA, a post-secondary education program serving individuals with intellectual disabilities. Dr. Reeves’ research focuses on behavioral strategies to support the inclusion of students with disabilities in school settings. Abigail Baxter, Ph. D., is a professor of Special Education for the College of Education and Professional Studies at the University of South Alabama in Mobile, Alabama. Dr. Baxter teaches a wide variety of undergraduate and graduate special education courses. She also serves as the PI for PASSAGE USA, a post-secondary education program serving individuals with intellectual disabilities. Dr. Baxter’s research has focused on effective early intervention practices for infants, toddlers, and their families as well as the impact of postsecondary education on young adults with intellectual disability.

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The Value of a Collaborative Approach to Addressing Executive Functioning Weakness in School-Based Intervention: An Occupational Therapy Perspective Michele M. Stillman, OTD, OTR/L, RN Allison Sullivan, DOT, MS, OTR/L Chris Alterio, Dr.OT, OTR/L American International College Abstract Executive functioning refers to a set of higher-order cognitive processes used for purposeful, goal-directed problem-solving. Executive functioning skills include a broad range of domains that are foundational to successful student performance. This research focuses on a subset of 145 school-based pediatric occupational therapists and their perceptions of how to address executive functioning weakness in school-aged occupational therapy participants. The results identified teachers as the primary providers of intervention for executive functioning weakness in students. The primary reason occupational therapists do not address executive functioning is because they view it as another discipline’s domain. This research identifies assessments and interventions used by occupational therapists to establish and address executive functioning weakness in school-aged students. The value of a collaborative approach to the identification and intervention of executive functioning weakness in students is reinforced, and potential contributions to assessment and intervention that occupational therapy can make are highlighted. The Value of a Collaborative Approach to Addressing Executive Functioning Weakness in School-Based Intervention: An Occupational Therapy Perspective Executive Functioning (EF) is a neuropsychological concept (Josman and Meyer, 2019) which refers to a set of higher-order cognitive processes that are involved in the regulation of attention, thoughts and actions (Wiebe & Karbach, 2018, p. 1). As the importance of this topic has increased, EF research has extended into exploring perspectives of developmental psychology as well as addressing issues of developmental psychopathology such as attentiondeficit/hyperactivity disorder (ADHD) and autism (Zelazo & Muller, 2002, p. 464). In the most current edition of the Occupational Therapy Practice Framework (OTPF-4), the mental functions of EF, including higher level cognition, attention, memory, perception, thought, mental functions sequencing complex movement, emotional and experience of self and time, are outlined (OTPF, 2020, pp. 51-52). Across professional disciplines, there are many EF domains. Scientists who study EF have found that three areas are most frequently referenced: working memory, inhibitory control, and cognitive or mental flexibility. (Shonkoff et al., 2011). Many domains of EF are interrelated, making it difficult to isolate them, both conceptually and operationally. As a result, some see EF as a unitary construct and choose to explore the association between individual domains (Jacob & Parkinson, 2015, p. 518). Regardless of the categorization or the labels, EF is essential to helping individuals accomplish everyday tasks (occupations). 154

There are many EF constructs that are noted in the literature across various professions, as well as numerous metaphors that can help people understand the complexity of EF. For example, Shonkoff et al. (2011) writes that “having EF in the brain is like having an air traffic control system at a busy airport to manage arrivals and departures of dozens of planes on multiple runways” (p. 1). Another useful metaphor likens EF functions to an executive assistant or secretary, making sure things are organized, planned and executed. Finally, Josman and Meyer (2019) suggest the metaphor of a sports coach leading the team towards the goal or function of the game (p. 86). The images elicited by these metaphors can provide a clearer picture of the significance of the role of EF in orderly and effective human performance. There is both a biological and environmental component to EF. Biologically, EF is strongly interconnected with the prefrontal cortex (PFC); the construct of EF developed out of looking at the consequences of PFC damage (Zelazo & Muller, 2002, p. 446). The PFC and its connections support a person’s ability to initiate and carry out cognitive processes that support new and goaldirected patterns of behavior, sustained attention, and motor attention, and are closely linked to emotional regulation as well (Siddiqui et al., 2008, p. 204). In addition to the biological component, the PFC can also be either positively or negatively influenced by a person’s environment, including factors such as socioeconomic status, stress, and caregiving quality (Wiebe & Karbach, 2018, p. 36-37; Raver, Blair, & Willoughby, 2013; Fay-Stammbach, Hawes & Meredith, 2014). EF can also be viewed from a developmental perspective. EF development is at its peak growth rate during early childhood, as children grow in flexibility and in their ability to choose their response to external stimuli. Development continues during middle childhood, including an increased ability to learn from feedback and increased processing speed. Many factors contribute to the development of healthy EF, including better coordination of control processes, increases in the strength of mental representation, and improvements in goal identification. These factors are a central part of the self-directed nature of EF, which becomes even more critical in adolescence, when young adults are using goal-directed behavior in order to achieve long-term life outcomes (Wiebe & Karbach, 2018, pp. 2-3). Taking into account the biological, environmental, and developmental aspects of EF, professionals from different disciplines have developed a number of approaches to identifying and addressing EF weakness. One model of EF was developed by Dawson & Guare (2009). Their model consists of 11 EF skills, which are divided into thinking skills and behavior skills. This model can also be organized developmentally and functionally, given the order in which skills emerge can help teachers to have realistic expectations of children at a particular age (Dawson & Guare, 2009, p. 15). Another approach to EF is based on Luria’s (1973) research that the “PFC and other neurological systems consist of interactive functional systems that involve the integration of subsystems” (Zelazo & Muller, 2002, p. 447). Zelazo & Muller (2002) built upon this understanding of EF as a function and not a mechanism or cognitive structure (p. 447). Based on the integration of subsystem components of EF, they proposed that EF varies according to the motivational significance of a situation. They suggested that EF characteristics can be elicited as either hot (more emotional/affective) or cool (less emotional/non-affective) (Zelazo & Muller, 2005, p. 71). Cool EF includes cognitive skills such as inhibitory control, working memory and cognitive 155

flexibility when used in affectively neutral situations, while hot EF includes the ability to delay gratification and affective decision making (What is executive functioning?, 2018). Occupational Therapy’s Approach The field of occupational therapy has a unique contribution to make to understanding and addressing executive functioning weakness. In order to understand how occupational therapy can be incorporated into EF intervention, it is first important to understand what the role of the occupational therapist is and the lens through which they view their clients. The Occupational Therapy Practice Framework: Domain and Process Fourth Edition (OTPF-4) provides a guide to OT practice. It defines the overarching goal of OT practice as “achieving health, well-being, and participation in life through the engagement in occupation” (OTPF-4, 2020, p. 5). The occupational therapy process includes analysis of client factors (the specific capabilities, characteristics or beliefs of a person), personal factors (the essence of who a person is), context (the environmental factors, including the physical, social, and attitudinal surroundings), and occupations (including activities of daily living, instrumental activities of daily living, health management, rest and sleep, education, work, play, leisure, and social participation) (OTPF-4, 2020, p. 7-15, 30-34). Occupational therapy practitioners are trained to provide effective intervention within these dynamic interactions to support their clients’ participation in daily living. OT services can be delivered through direct individual or group services, through a collaborative approach, and through indirect services such as consultation on a client’s behalf (OTPF-4, 2020, p. 18-19). The role of the occupational therapist is further refined based on the setting in which they practice. School-based occupational therapy practitioners recognize that a primary role and occupation of a child is being a student (Benson, p. 1). Occupational therapy practitioners who work in schools “use meaningful activities (occupations) to help children and youth participate in what they need and/or want to do in order to promote physical and mental health and well-being” (AOTA, 2017). Intra- and interprofessional collaborations are a key component in the service delivery of school-based OT services (OTPF-4, 2020, p. 18). As of the 2018-19 school year, more than 7.5 million children with disabilities were eligible for early intervention, special education, and related services (IDEA, 2021). According to Section 1412(a)(5) of the IDEA, children with disabilities should be included in the general education environment as much as reasonably possible, using whatever supplementary aids and services are necessary to support this level of participation (IDEA, 2019). Occupational therapists are integral in facilitating this level of inclusion for students with disabilities. EF and the School-Based OT EF skills are essential for building children’s academic and social skills, as well as building their capacity for functional independence. For children and adolescents, EF supports the processes of learning, remembering, planning and making decisions, enabling them to acquire knowledge and to participate in learning, playing and social interactions (Shonkoff et al., 2010). Due to EF’s central role in child development, EF weakness is a major risk factor for academic failure and compounding negative outcomes (Daly, Delany, Egan & Baumeister, 2015; Moffitt et al., 2011). Cool EF (more cognitively-based skills) has been found to be more strongly 156

associated with children’s academic achievement, while hot EF (more emotionally-based skills) has been found to be more strongly implicated in children’s disruptive and social behavior (Brocki, Nyberg, Thorell, & Bohlin, 2007; Garner & Waajid, 2012; Willoughby, Kupersmidt, Voegler-Lee, & Bryant, 2011). Despite the central role of EF to a child’s academic and social development, OTs have been underutilized in addressing EF weakness. The role of the school-based OT is often seen as addressing fine motor classroom skills, handwriting, and sensory processing, as Bolton & Plattner (2020) found in their survey of OT’s and teachers (p. 141). They also found that schoolbased OTs rarely received referrals for general school environmental navigation, social interactions, or life skills (Bolton & Plattner, 2020, p. 141). A further outcome of Bolton & Plattner’s (2020) survey revealed that teachers found OT services to be valuable, but of the 47 respondents, only 11% reported having a collaborative relationship. It is unclear whether teachers had a full and accurate understanding of the varied services and supports an OT can provide. While OTs may be underutilized in addressing EF weakness, some of the responsibility lies with the profession’s lack of clarity regarding EF assessment and intervention. There is no standard definition for EF in the occupational therapy literature. Josman & Meyer (2019) reviewed 50 publications and found over 40 referenced definitions for EF or its cognitive components (p. 80). Furthermore, many studies indicate that OTs would benefit from expanding their evaluations so that they include data about children’s cognition and executive functioning (Rosenberg, Jacobi, & Bart, 2017; Zingerevich & LaVesser, 2009), and then developing and delivering services to address EF in an interprofessionally collaborative classroom environment. A clearer understanding of the importance of EF, along with an expansion of OT involvement and increased collaboration among the school team would allow for greater generalization of EF skills to other settings and activities for students. The complexities of addressing EF call for a multidisciplinary approach. Occupational therapy, due to its grounding in science and its incorporation of both environmental and personal factors, can bring a unique perspective to addressing EF. Given the importance of EF to a child’s academic and social development, it is imperative that the professional roles of each discipline be examined in the context of addressing EF. It is also important that school teams have a clear approach to intervention, and that they provide evaluation and intervention as needed to students who may have significant weakness that is negatively impacting their ability to participate in learning. Introduction to Study This qualitative study explored how pediatric occupational therapy practitioners (n=145) address executive functioning weakness in school-aged OT participants. This article will focus on a subset of the data which reports the perceptions of pediatric school-based occupational therapy practitioners and their role with EF assessment and intervention in school practice. This article will also provide a better understanding of what occupational therapists can contribute to in addressing this important aspect of a child’s development, and will present considerations for a more collaborative approach to addressing EF weakness.

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Methods The study received approval through American International College’s Institutional Review Board (IRB). A qualitative online survey consisting of 25 questions was created using Microsoft Forms, to which 200 OT practitioners responded between October and December of 2020. Of the 193 usable responses, 145 of the respondents identified themselves as schoolbased occupational therapy practitioners. The subset of 145 respondents is the focus of the data analysis. Participants were recruited online through cluster sampling, snowball sampling, and networking. It was not known how many individuals were reached via social media efforts; therefore, the researcher could not calculate an exact response rate. The initial question outlined the purpose of the survey, explained inclusion criteria, and solicited consent for participation. The inclusion criteria were English-speaking pediatric and school-based occupational therapy (OT) practitioners who were currently working or who had worked in the past five years. Survey data that was obtained was categorized and stored for analysis using Microsoft Forms. Results Demographic Data Of the 145 school-based participants, 122 identified themselves as school-based pediatric occupational therapists and 23 identified themselves as school-based pediatric occupational therapy assistants. Based on the US census bureau’s division of regions in the United States, 7% of the participants were from the Midwest (n = 11), 65% from the Northeast (n = 94), 9% from the South (n = 13), and 19% from the West (n = 27). These participants reported their levels of experience in pediatric practice as follows: new to practice through 5 years of experience 33% (n = 48), 6-10 years of experience 18% (n = 26), 1120 years of experience 32% (n = 47), and 21+ years of experience 17% (n = 24). Provision of EF Services Based on the survey results, teachers assumed the primary role of addressing EF weakness (41%, n = 60), followed by OT’s (23%, n = 33), and then followed in hierarchal order by psychologists (9%, n = 13), speech-language pathologists (8%, n = 11), school counselors (7%, n = 10), occupational therapy assistants (6%, n = 8), a team approach (3%, n = 5), other intervention specialists (3%, n = 4), and no provider (1%, n = 1) (Figure 1).

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Figure 1. Primary provider of services for EF weakness as reported by school-based occupational therapy practitioners (n = 145). Participants were asked to list possible reasons why OTs were not addressing EF. Seventy-two percent of participants responded that it is viewed as the domain of another discipline (n = 104), 66% responded that it is due to a lack of education and training (n = 95), 51% responded that it is not prioritized as a need (n = 74), 40% responded that it is because of a lack of support from family or school (n = 58), and 6% responded that it is not applicable to students they treat (n = 9) (Figure 2).

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Figure 2. Possible reasons OTs are not addressing EF, as reported by school-based occupational therapy practitioners (n = 145). Students Requiring EF Intervention The percentage of respondents who reported providing EF intervention for students with the following diagnoses or conditions is as follows: ADD/ADHD (98%, n = 142), autism (95%, n = 138), leaning disabilities (88%, n = 128), anxiety (69%, n = 97), TBI/concussion (50%, n = 72), conduct disorder (48%, n = 70), fetal alcohol spectrum disorder (45%, n = 65), OCD (44%, n = 64), depression (35%, n = 51), and other diagnoses or conditions (4%, n = 6) (Figure 3).

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Figure 3. Diagnoses and conditions of students requiring EF intervention, as reported by schoolbased occupational therapy providers (n = 145). Assessment and Interventions The survey categorized assessments that could be used by occupational therapists to evaluate EF. For each category, specific assessments were listed to provide examples, and respondents were encouraged to indicate all the assessments that they use. Of the 145 respondents, 84% utilized more than one method of assessment (n = 122). The remaining 16% reported use of one assessment tool (n = 23). Sensory processing was the most frequent assessment used (75%, n = 109), followed by visual motor integration assessments (74%, n = 107), the use of observations (64%, n = 93), visual perceptual processing assessments (33%, n = 48), executive function assessments (19%, n = 28), self-regulation scales (6%, n = 8), cognitive assessments (4%, n = 6), and other assessments (6%, n = 8) (Figure 4).

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Figure 4. Assessments completed by OTs to assess EF, as reported by school-based occupational therapy practitioners (n = 145). OTs employ a variety of interventions to address EF weakness. For each intervention categorized, specific tools were listed to provide examples, and respondents were encouraged to indicate all the interventions that they use. Of the 145 respondents, 95% use visual aids (n = 138), 89% use environmental modifications (n = 129), 89% use activity breaks (n = 129), 76% use organizational helps (n = 110), 73% use writing supports (n = 106), 63% use self-monitoring skills (n = 91), 39% use note taking supports (n = 56), 17% use reading supports (n = 24), and 8% use other interventions (n = 11) (Figure 5).

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Figure 5. EF interventions used by school-based OT practitioners, as reported by school-based occupational therapy practitioners (n = 145). Discussion Collaborative Approach The literature across the various professions shows EF to be a complex topic, with biological components associated with the PFC and its subsystems, environmental influences such as stress and caregiving quality, and developmental factors as well, which collectively affect individuals both cognitively and emotionally. Given this level of complexity, it is no wonder that there are many different professionals in the school system who may be the primary provider of services for students with EF weakness. As this research showed, depending upon the school, it may be a teacher, occupational therapist, psychologist, speech/language pathologist, school counselor, social worker, or other professionals who take the lead in providing services (Figure 1). Shonkoff et al. (2011)’s analogy of EF as an air traffic control system at a busy airport is an apt one. Many different systems are monitored and supported in order to manage the traffic. Each of the above disciplines have valuable contributions they can make to addressing EF from their own professional lens, and each of their models has strengths and weaknesses. The trouble comes when one or more of the school professionals does not add their strengths to the EF assessment and intervention because they view EF as the domain of another professional discipline. As this research showed, the number one reason OT practitioners are not addressing EF is because they view it as the domain of another discipline (Figure 2). The researcher views this is a grievous mistake, in that it leads to the student’s EF weakness not being comprehensively addressed. Furthermore, given the secondary reason OT practitioners report not addressing EF is that they believe they lack the relevant education and training, it is important for OT practitioners to pursue additional training in order to contribute to addressing EF. 163

Since EF is such a complex concept involving both cognitive and emotional dimensions, it is best addressed holistically and comprehensively by the team of school professionals, with OTs playing a pivotal role. In this research, only 3% (n=5) of OT practitioners reported that the primary provider of EF services was the team of school professionals. A team approach, however, increases the likelihood that interventions will address not only academic weaknesses such as math and writing (the primary domain of the teacher), but life skill tasks as well (the primary domain of the OT practitioner). A systematic review of the literature about the association between EF and student achievement done by Jacob & Parkinson (2015) found that although there is no definitive evidence that improving EF causes increased academic achievement, there is evidence that when interventions influence EF and academic achievement simultaneously, there is a positive impact on academic achievement (p. 541). Furthermore, since environmental factors such as stress, loneliness, and lack of physical fitness also negatively impact EF, addressing children’s physical, emotional, and social development may be the best way to improve EF and academic achievement (Diamond & Lee, 2011, p. 7). This supports the conclusion that a holistic and comprehensive approach will provide the most benefit to the student, as the skills they learn will have a greater opportunity not only for academic improvement but for generalization into other aspects of their life. Barriers to collaboration among teachers and occupational therapists are well documented in literature. Lombardi & Hunka (2001) point out that “many general education teachers in inclusive classrooms may not have been adequately trained or prepared for educating students with disabilities or working with related service professionals” (p. 183-4). Huang et al., (2010) conducted a focus group with teachers and found that they lacked a full understanding of the role of OT and its benefits for students. This lack of understanding, along with communication issues, poor timing and a lack of scheduled meetings, unclear communication of intervention or review of intervention progress, and only brief periods of time in the classroom, limited the collaborative relationship (p. 84). As illustrated in collaborative models such as Partnering for Change (P4C), taking the time to establish relationships with teachers and the school community is essential, particularly the importance of being inclusive and providing consistent and responsive services (Campbell et al., 2012, p. 55). The hope is that as the teacher has more exposure to and positive experiences with OTs, effective collaboration will increase. This is supported by research done by Huang et al. (2010), who found that special educators can be better prepared than general education teachers to carry out OT recommendations because they have observed more OT intervention (p. 85). One final factor in increasing successful collaboration is when the school administration values and promotes a collaborative approach (Rose & Seruya, 2020, S1). Diagnoses In research by Rapoport et al. (2016), typical academic issues such as reading, writing, and arithmetic tend to be addressed by teachers (p. 10). When students have disabilities, however, underlying issues can often impact their academic participation and success. Rapaport et al. (2016) identified the fact that teachers are less likely to identify EF as a contributor when students are experiencing academic weakness and, unless they have been trained in EF, their knowledge tends to be intuitive (p. 10). This reality highlights the importance of a comprehensive approach that includes the unique contributions of OT.

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In reviewing common disabilities that impact students’ participation in their school experience, ADHD is notable for impairment in features of EF, particularly working memory, which is important for attentional control and learning (Martinussen & Major, 2011, p. 68). In this research, ADD/ADHD was the primary diagnosis of students requiring EF intervention noted by OT practitioners, followed by autism and learning disabilities. Other diagnoses that were less common were mental health disorders such as anxiety, conduct disorder, OCD and depression; neurocognitive disorders such as TBI/concussion; and neuropsychological disorders such as fetal alcohol syndrome disorder (Figure 3). Given that teachers are not always adequately trained in identifying, addressing, and providing intervention for EF, students who have diagnoses with potential EF-related issues should be referred to OT for possible early identification of issues before they issues intensify. This baseline data can be helpful to provide a reference point for the sake of comparison as the student develops. Assessment In this study, 84% of school-based occupational therapists reported that they tend to employ a variety of assessment tools to evaluate a student’s EF function. The assessments most often completed by OTs in order to assess EF function were sensory processing measures, visual motor integration measures, observations and visual perceptual processing measures. Only 28% of occupational therapists incorporated the use of formal executive functioning measures, and to a much lesser degree self-regulation scales (8%), and cognitive assessments (6%) were utilized. It is possible that the latter two areas for assessment, self-regulation scales and cognitive assessment, may have been addressed by either the school’s psychologist or speech language pathologist and an occupational therapy assessment in those areas may have been considered redundant (Figure 4). The responses from this research did not align with the occupational therapy literature research review by Josman and Meyers (2018). In their research, the tools most commonly noted in OT literature were the Behavior Rating Inventory of Executive Function (BRIEF) and the Behavioral Assessment of the Dysexecutive Syndrome for Children (BADS-C), both of which would be classified as executive functioning measures (p. 80). However, there is justification for using sensory processing measures to assess EF weakness. Diamant et al. (2020) found that increased sensory reactivity was correlated with decreased abilities for EF and effortful control, as well as with increased impulsivity, reduced attention, and decreased on-task behavior (p. S1). In order to provide a more occupation-based perspective to function while remaining grounded in the OT professional framework, school-based occupational therapists should consider using more performance-based assessments that use real-world tasks in the correct environmental context (Gillen, 2013, p. 647-8). While this research focused on types of assessments but did not collect data on specific measures used by OT, it is important to consider ecology when choosing EF assessments, so that the results will have high verisimilitude. The two most common tools noted by Josman & Meyer (2018), the BRIEF and the BADS-C, are in the middle of the ecological continuum. Performance-based assessments that rate as highly ecological include the Children’s Cooking Task, Children’s Kitchen Task Assessment, Do-Eat, School Function Assessment, and the Weekly Calendar Planning Activity (Josman & Meyer, 2018, p. 84). 165

Wallisch et al. (2018) conducted a scoping review to examine the literature related to the ecological validity of tasks and behavioral assessments used to measure aspects of EF in children. Their findings showed that the BRIEF-P, BRIEF-SR, and BADS-C were the most widely cited assessments throughout this review (Wallisch et al., 2018, p. 9). In the five studies Wallisch et al. (2018) reviewed, the BRIEF, which is considered an ecological valid measure, was compared to traditional measures, many that used correlations with the BRIEF. However, this review found that these traditional assessments had no correlation with the BRIEF, except for one measure, which had partial correlation (Wallisch et al., 2018, p. 10). Therefore, questionnaires such as the BRIEF, which assess a broad range of concerns, should not take the place of performance-based or other specific developmentally appropriate assessments, but should rather be used as a complementary piece (Chevignard et al. 2012, pp. 1051-2). Intervention There is limited literature about the various models and interventions related to EFs in occupational therapy (Josman & Meyer, 2018, p. 86). This research found that OTs employ a variety of interventions to address EF weakness, with the most common being visual aids, environmental modifications, activity breaks, organizational helps, writing supports, and selfmonitoring skills (Figure 5). These interventions align well with the interventions used by teachers, according to Martinusseen & Major (2011). These researchers found that teachers support students with working memory difficulties by breaking up complex tasks, delivering instructions one piece at a time, giving the students time to process the information, and using external memory aids (Martinusseen & Major 2011, p. 70). This is understandable, as teachers are the primary educator; however, it is within the scope of OT to support teachers by providing teachers with these interventions, or by helping students with organizing materials, using checklists, and giving instructions in goal-setting and planning, thereby freeing up teachers to assist in areas that are more academically focused (Martinussen & Major, 2011, p. 71). Another intervention that can be used with students with EF weakness is scaffolding, which involves breaking up tasks into manageable chunks. Meuwiseen & Zelazo (2014) used the categorizations of EF as hot (more emotional/affective) or cool (less emotional/non-affective). When addressing hot EF, if a student’s environment is deemed distractable and is potentially impacting their ability to functionally participate in a task, this could be scaffolded by a practitioner removing some of the distraction in order to reduce the amount they would need to self-regulate. With regards to cool EF, if a student is having difficulty following directions in order to complete a task, this task might be scaffolded by providing the student with one or two directions at a time instead of three or more (Meuwissen & Zelazo, 2014, p. 21). Top-down approaches, which involve interventions based on a student’s functional status as opposed to specific components, can also be used. Espinet et al. (2013) examined the cognitive neuroscience of EF, and identified neural findings that support the value of reflection training with preschool children in order to help them approach goals in a more top-down manner. This training can support children in identifying conflicts and inconsistencies in their approaches so that they can work to resolve them. This study verifies that EF can be trained using a brief intervention that targets reflection (Espinet et al., 2013, p. 14).

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Regardless of the interventions used, more research needs to be done on the benefit of EF intervention to academic achievement. Jacob & Parkinson (2015) conducted a systematic review of the literature examining the correlation between EF and academic achievement in math and reading, which revealed that there is a moderate unconditional association between EF and academic achievement that was not influenced by age or EF construct. Notably, they found no clear evidence of a causal relationship between EF and academic achievement (Jacob & Parkinson, 2015, p. 512). Limitations In this research, a number of methodological limitations have been identified, including a small and not geographically diverse sample size and distribution of the survey through social media. In addition, the survey was initially designed to target pediatric occupational therapy practitioners and not solely focus on school-based occupational therapy practitioners. Another limitation is that there could be bias based on different schools having different protocols and policies that staff are required to follow in their school system. Lastly, the survey was conducted during the COVID-19 pandemic, where service delivery changes occurred, which may be a factor in how practitioners responded. Conclusion The results of this study highlight the benefits of interprofessional collaboration when identifying and addressing the complexities of EF weakness in school-aged participants. This study identified teachers as the primary providers of intervention for EF weakness in students. Despite their skill and scope of practice to address EF, OTs may not be included to their fullest capacity, as even many OTs view EF as the domain of another discipline. Occupational therapists can make unique contributions to assessment and intervention as well as support and enhance EF intervention in the school setting. There is a clear need for further research, particularly in the areas of the effectiveness of collaborating as a team to address EF and to more fully understand how to best assess and provide intervention in the area of EF. References AOTA. (2017). What is the role of the school-based occupation therapy practitioner? AOTA.org. https://www.aota.org/~/media/Corporate/Files/Practice/Children/SchoolAdministrator-Brochure.pdf. Benson, J. D., Szucs, K. A., & Mejasic, J. J. (2016). Teachers’ perceptions of the role of occupational therapist in schools. Journal of Occupational Therapy, Schools, and Early Intervention, 9(3), 290-301. https://doi.org/10.1080/19411243.2016.1183158 Bolton, T., & Plattner, L. (2020). Occupational therapy role in school-based practice: Perspectives from teachers and OTs. Journal of Occupational Therapy, Schools, & Early Intervention, 13(2), 136-146. https://doi.org/10.1080/19411243.2019.1636749 Brocki, K. C., Nyberg, L., Thorell, L. B., & Bohlin, G. (2007). Early concurrent and longitudinal symptoms of ADHD and ODD: Relations to different types of inhibitory control and working memory. Journal of Child Psychology and Psychiatry, 48(10), 1033– 1041. https://doi.org/10.1111/j.1469-7610.2007.01811.x 167

Campbell, W. N., Missiuna, C. A., Rivard, L. M., & Pollock, N. A. (2012). “Support for everyone”: Experiences of occupational therapists delivering a new model of schoolbased service. Canadian Journal of Occupational Therapy, 79(1), 51-59. https://doi.org/10.2182/cjot.2012.79.1.7 Chevignard, M. P., Soo, C., Galvin, J. Catroppa, C., & Eren, S. (2012). Ecological assessment of cognitive functions in children with acquired brain injury: A systematic review. Brain Injury, 26, 1033-1057. https://doi.org/10.3109/02699052.2012.666366 Daly, Delany, Egan & Baumeister (2015). Childhood self-control and unemployment throughout the life span: evidence from two British cohort studies. Psychological Science, 26(6), 709-723. https://doi.org/10.1177/0956797615569001 Dawson, P. & Guare, R. (2009). Smart but scattered. The Guilford Press. Diamant, R., Ruiz, H., & Holmes, K. (2020). Relationships between sensory processing behaviors, executive function, and temperament characteristics for effortful control in school-age children. The American Journal of Occupational Therapy, 74, S1. https://doi.org/10.5014/ajot.2020.74S1-PO9119 Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333, 959-964. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3159917/. Espinet, S. D., Anderson, J. E., & Zelazo, P. D. (2013). Reflection training improves executive function in preschool-age children: Behavioral and neural effects. Developmental Cognitive Neuroscience, 4, 3-15. https://dx.doi.org/10.10106/j.dcn.2012.11.009 Fay-Stammbach, T., Hawes, D. J., & Meredith, P. (2014). Parenting influences on executive function in early childhood: A review. Child Developmental Perspectives, 8(4), 258-264. https://doi.org/10.1111/cdep.12095 Gillen, G. (2013). A fork in the road: An occupational hazard? (Eleanor Clarke Slagle Lecture) American Journal of Occupational Therapy, 67(6), 641-652. https://doi.org/10.5014/ajot.2013.676002 Huang, Y., Peyton, C. G., Hoffman, M., & Pascua, M. (2010). Teacher perspectives on collaboration with occupational therapists in inclusive classrooms: A pilot study. Journal of Occupational Therapy, Schools, & Early Intervention, 4(1), 71-89. https://doi.org/10.1080/19411243.2011.581018 IDEA. (2021). About IDEA. IDEA: Individuals with Disabilities Education Act. Retrieved February 27, 2021 from https://sites.ed.gov/idea/about-idea/. IDEA. (2019). Section 1412(a)(5). IDEA. Individuals with Disabilities Education Act. Retrieved February 27, 2021 from https://sites.ed.gov/idea/statute-chapter-33/subchapterii/1412/a/5. Jacob, R., & Parkinson, J. (2015). The potential for school-based interventions that target executive function to improve academic achievement: A review. Review of Educational Research, 85(4), 512-552. https://doi.org/10.3102/0034654314561338 Josman, N. & Meyer, S. (2019). Conceptualisation and use of executive functions in paediatrics: A scoping review of occupational therapy literature. Australian Occupational Therapy Journal, 66, 77-90. https://doi.org/10.1111/1440-1630.12525 Lombardi, T. P. & Hunka, N. J. (2001). Preparing general education teachers for inclusive classrooms: Assessing the process. Teacher Education and Special Education, 24, 183197. https://doi.org/10.1177/088840640102400303 Luria, A. R. (1973). The working brain: An introduction to neuropsychology, trans. B. Haigh. New York: Basic Books. 168

Martinussen, R. & Major, A. (2011). Working memory weaknesses in students with ADHD: Implications for instruction. Theory into Practice, 50, 68-75. https://doi.org/10.1080/00405841.2011.534943 Meuwissen, A. S., & Zelazo, P. D. (2014). Hot and cool executive function: Foundations for learning and healthy development. Zero to Three, 35(2) 18-23. Retrieved from https://eric.ed.gov/?id=EJ1125267. Rapoport, S., Rubinsten, O., & Katzir, T. (2016). Teachers’ beliefs and practices regarding the role of executive functions in reading and arithmetic. Frontiers in Psychology, 7, 1-14. https://doi.org/10.3389/fpsyg.2016.01567 Raver, C. C., Blair, C., & Willoughby, M. (2013). Poverty as a predictor of 4-year-olds' executive function: new perspectives on models of differential susceptibility. Developmental psychology, 49(2), 292–304. https://doi.org/10.1037/a0028343 Rose, E., & Seruya, F. (2020). Collaboration, the elusive phenomenon: Perceptions of OTs in the school setting. The American Journal of Occupational Therapy, 74, S1. https://doi.org/10.5014/ajot.2020.74S1-PO3315 Rosenberg, L., Jacobi, S., & Bart, O. (2017). Executive functions and motor ability contribute to children’s participation in daily activities. Journal of Occupational Therapy, Schools, & Early Intervention, 10(3), 315-326. https://doi.org/10.1080/19411243.2017.1312660 Shonkoff, J. P., Duncan, G. J., Fisher, P.A., Magnuson, K. & Raver, C. (2011). Building the brain’s ‘air traffic control’ system: How early experiences shape the development of executive function (Working Paper No. 11). Harvard University Center on the Developing Child. http://www.developingchild.harvard.edu. Siddiqui, S. V., Chatterjee, U., Kumar, D., Siddiqui, A., & Goyal, N. (2008). Neuropsychology of prefrontal cortex. Indian journal of psychiatry, 50(3), 202–208. https://doi.org/10.4103/0019-5545.43634 Wallisch, A., Little, L. M., Dean, E., & Dunn, W. (2018). Executive function measures for children: A scoping review of ecological validity. OTJR: Occupation, Participation and Health, 38(1), 6-14. https://doi.org/10.1177/1539449217727118. What is executive functioning? (2018). Learning Success. Retrieved February 21, 2021 from https://www.learningsuccesssystem.com/ldinfo/ef/what-executive-functioning. Wiebe, S.A. & Karbach, J. (2018). Executive function: Development across the life span. Routledge. Willoughby, M., Kupersmidt, J., Voegler-Lee, M., & Bryant, D. (2011) Contributions of hot and cool self-regulation to preschool disruptive behavior and academic achievement. Developmental Neuropsychology, 36(2), 162-80. https://doi.org/10.1080/87565641.2010.549980 Zelazo, P. D., & Müller, U. (2002). Executive function in typical and atypical development. In U. Goswami (Ed.), Blackwell handbooks of developmental psychology. Blackwell handbook of childhood cognitive development (p. 445–469). Blackwell Publishing. https://doi.org/10.1002/9780470996652.ch20 Zelazo, P. D., & Müller, U. (2005). Hot and cool aspects of executive function: Relations in early development. In W. Schneider, R. Schumann-Hengsteler, & B. Sodian (Eds.), Young children’s cognitive development: Interrelationships among executive functioning, working memory, verbal ability, and theory of mind. Psychology Press.

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Zingerevich, C., & LaVesser, P. D. (2009). The contribution of executive functions to participation inn school activities of children with high functioning autism spectrum disorder. Research in Autism Spectrum Disorders, 3, 429-437. About the Authors Michele Stillman, OTD, OTR/L, RN, has practiced as an occupational therapist for the past 25 years in a variety of practice areas. She is currently the Occupational Therapy Supervisor at Capitol Region Mental Health Center, an outpatient mental health center for young adults in Hartford, Connecticut. Michele graduated from American International College with her PostProfessional Occupational Therapy Doctorate, with her research focused on addressing executive functioning weakness in school-based intervention. Her passion for this subject grew out of wanting to contribute to improving intervention for her children as well as other students with executive functioning weakness. She draws upon attachment theory for the foundation of her therapeutic work, and is a dedicated member of the Attachment Network of Connecticut. Allison Sullivan, OT, DOT, OTR, is an Associate Professor at American International College in Springfield, Massachusetts. Dr Sullivan serves as lead faculty for AIC's Post-Professional Doctor of Occupational Therapy program. Her research interests include pedagogy in occupational therapy education, cognitive disabilities, and trauma-informed care. Christopher Alterio, Dr.OT, OTR/L, is the founding Dean of Health and Human Services and Professor of Occupational Therapy at Keuka College in Keuka Park, New York.

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