British Journal of Educational Technology doi:10.1111/j.1467-8535.2010.01104.x
Vol 42 No 5 2011
The effect of learning style on preference for web-based courses and learning outcomes _1104
Nick Z. Zacharis Nick Z. Zacharis is an assistant professor in the Technological Educational Institute of Piraeus. His research interests include web-mining, machine learning, human-computer interaction, internet technologies and e-learning. Address for correspondence: Nick Z. Zacharis, Technological Educational Institute of Piraeus, Department of Mathematics, Computer Science Division, Petrou Ralli & Thivon 250, Athens 12244, Greece. Email: email@example.com
Abstract As the number of courses offered online increases rapidly, it is important for teachers and institutions to identify specific learner characteristics of successful online students. This paper reports on a study that compared an online group of freshmen computer science majors with an equivalent on-campus group to find if their individual learning styles play a role in the selection of course delivery mode (online or face to face) and in their academic achievement. No significant statistical differences were detected in learning styles and learning performance between the two groups. Implications for teaching practice and design of learning activities that resulted from this study are discussed. Introduction In this age of learner-centred learning, online instruction provides a unique opportunity for learning materials, tasks, and activities to fit individual learning styles and preferences (Bonk, Wisher & Lee, 2003). The non-linear access of the different types of online digital resources allows students to take control over the learning process, engage in social interaction and dialogue, develop multiple modes of representation, and become more self-aware (Oliver & McLoughlin, 1999). However, the freedom and flexibility provided by the online environment have as a side effect that many students are pursuing online learning opportunities only for the sake of convenience without any real consideration of the appropriateness of this delivery mode for their individual learning styles. ‘Those students who may not have developed appropriate strategies for self-regulation may find that online education courses do not meet their needs and those students may subsequently drop the course; as a consequence, online courses have been associated with much higher rates of attrition than traditional face-to-face courses’ (Summers, Waigandt & Whittaker, 2005). The current literature does not rule out the possibility that there may be only certain types of students who can successfully learn via the online format (Aragon, Johnson & Shaik, 2002; Boyd, 2004; Meyer, 2003). In general, there is evidence that students with a more independent learning style, greater self-regulating behaviour and the belief they can learn equally well through this modality are more successful in the online environment (Meyer, 2003). An analysis of the relationship between student learning styles, preference for delivery mode and course achievement will provide administrators with the vital information they need to prepare courses that cater the needs of students involved. Despite literature on the effectiveness of online instruction, little is known about the influence of learning styles in online learning (Battalio, 2009; Means, Toyama, Murphy, Bakia & Jones, 2009). Little research also exists on how computer science students learn in different learning environments (Byrne & Lyons, 2001; Goold & Rimmer, 2000). © 2010 The Author. British Journal of Educational Technology © 2010 Becta. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
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In this light, the purpose of this study was (1) to determine if learning style is a predictor of students’ preference for online versus face-to-face delivery format, and (2) to compare students’ achievement (on course grades) in two different learning environments—online instruction and on-campus/face-to-face instruction—based on their individual learning styles. Learning environments and learning styles In the last two decades web-based learning environments have become increasingly pervasive in higher education. Not only courses offered partly or fully online use the Internet and web technologies to deliver instruction to the online classes, but even face-to-face classes have online components that complement the classroom activities. Online classes use websites that provide a user-friendly interface and easy access to text, graphics, audio, and video materials that may be used and managed in a consistent and convenient manner. Usually, these websites include basic course information such as syllabus, announcements lists, instructor notes and links to other digital resources, and very often integrate tools for synchronous or asynchronous communication, streaming video, and file/applications sharing. Online learning is different than traditional classroom-based learning. This is mostly due to the fact that teachers and students do not have face-to-face contact. Thus, the teachers can have little control over their students’ learning situations. Online components are accessible when the student needs them and learning is self-paced, providing students the chance to identify their learning goals and objectives and create their own path through course material. Although more flexible than the conventional classroom learning, the online environment increases complexity (Ellis & Kurniawan, 2000). Students are forced to determine their own learning strategies and manage their time and resources, and therefore, those who lack the skills for self-regulation and a deeper understanding of their preferred learning styles may find the online environment difficult and become confused. Coming from different home experiences and educational backgrounds, students vary considerably in how they approach learning and, consequently, in how they should be taught. Some students learn best interactively and some individually, others focus on facts and data while others are interested in theories and concepts, some prefer visual forms of information and some respond better to written and spoken explanations (Mupinga, Nora & Yaw, 2006). Many educators agree that understanding differences in students’ learning styles is an important aspect of effective learning and teaching. Learning styles refer to the different ways learners use to perceive, process and conceptualise information. When students know and understand their learning styles, they can modify their tactics to increase academic achievement. Being aware of his or her students’ different learning styles, the instructor can adjust his or her teaching style to their different academic skills and interests. There are several models of learning styles that are currently being used to assess how students learn. A learning style model ‘classifies students according to where they fit on a number of scales pertaining to the ways they receive and process information’ (Felder & Silverman, 1988). Identifying and accommodating diverse learning styles is a hard task in any classroom environment (Gilbert & Han, 1999). While in a face-to-face classroom environment experienced teachers almost instinctively incorporate different teaching strategies to address student’s needs, in the online environment conscious effort must be employed to organise materials and activities in order to accommodate the variety of learning styles possessed by their students. Kolb’s model, preference for course format and learning performance Grounded on experiential learning theory, Kolb’s model of learning styles has survived examination and criticism over the years and is used extensively to categorise the way learners take in and © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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process information in traditional or web-based environments. According to Kolb (1984) individuals learn in four stages or modes: Concrete Experience (CE, eg, specific examples, fieldwork, observations or video), Reflective Observation (RO, eg, journals, discussion groups, brainstorming, problem solving), Abstract Conceptualisation (AC, eg, papers, lectures, analogies, model building) and Active Experimentation (AE, eg, laboratories, simulations, case study, homework). However, the process of constructing knowledge in different learning situations involves a creative combination among the four learning modes that is responsive to contextual demands. The combination of learning modes are used to establish four quadrants, reflecting four learning styles: Accommodators (favoured CE and AE, ie, feeling and doing), Divergers (favoured CE and RO, ie, feeling and watching), Assimilators (favoured AC and RO, ie, thinking and watching) and Convergers (favoured AC and AE, ie, thinking and doing). A number of studies have examined the association between students’ learning style and the selection of course delivery format. However, the research outcomes have provided mixed results. Terrell (2002) tracked 159 doctoral students, majoring in Computing Technology in Education, during their coursework in an online learning environment and showed that learning style determines preference for online delivery format. Students with a preference for AC (Convergers and Assimilators) preferred online learning and were more likely to succeed than students preferring CE (Divergers and Accommodators). Aragon et al (2002) also found significant differences in learning styles between traditional, face-to-face students and online learners. Online learners were more likely to prefer reflective observation and AC (Divergers and Assimilators), while face-to-face students were more likely to prefer AE (Accommodators and Convergers). Buerck, Malmstrom & Peppers (2003) compared 13 students in the online section versus 16 in the face-to-face section of a computer science course and found significant differences in learning styles between online students (tended to have the Converger learning style) and their traditional counterparts (were more likely to have the Assimilator learning style). In contrast, Oh and Lim (2005) concluded that students’ learning styles were not significantly correlated with their attitudes and preference for instructional delivery modes while other factors such as previous online learning experience and computer competency were significantly correlated with students’ learning outcomes and attitudes towards online instruction. Brittan-Powell, Legum and Taylor (2008) conducted a research study to find out whether students’ preference for course delivery modality (fully online vs. face to face) was contingent upon their Kolb learning style. A 2 by 4 (course delivery format by Kolb’s learning styles) chi-square test was used to compare the learning styles of 72 online students with that of 36 traditional students, all of whom had self-selected their instructional environment. Results revealed that no unique relationship exists between student learning style and their selection of a traditional face-to-face course compared with a fully online course: c2 (3, N = 108) = 3.22, p = 0.21. The majority of the research studies of learning outcomes have shown that there was no significant difference in students’ learning achievement between online and face-to-face instruction in terms of the effects of the learning styles (Aragon et al, 2002; Brittan-Powell et al, 2008; Buerck et al, 2003; Oh & Lim, 2005). Aragon et al (2002) emphasised online learning achievement and suggested students learn as effectively in an online environment as in conventional classroom settings regardless of learning style preference across motivation, task engagement strategies, and cognitive processing habits (cognitive controls). Diaz and Cartnal (1999) suggest that if there are no differences in learning styles, then the same learning activities should be effective for both traditional and online classroom, and any differences in the outcomes are due to other factors. Although McNeal and Dwyer (1999) found no significant difference in learning between instruction that was designed in agreement with students’ learning styles and instruction designed in disagreement with students’ learning styles, © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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not all learning styles perform the same in online and traditional course format. Benham (2002) compared the effectiveness of online training and traditional training in a computer literacy course using the Kolb Learning Style Inventory. Scores on lab exercises and exams were used to measure training effectiveness for each of the four Kolb learning styles of online and traditional students. The author suggests that learning styles do influence learning performance and concludes that the online group was significantly less effective for students who favour a learning style which include AC (ie, Convergers and Assimilators). However, Manochehr (2006) also compared online learning versus traditional instructor-based learning, based on students learning styles, and showed that the learning style in traditional learning was irrelevant but in online learning it was very important. Students with learning styles Assimilator and Converger did better with the online learning method while students with learning styles Accommodator and Diverger received better results with traditional instructor-based learning. Research questions The aforementioned studies generate inconsistent empirical evidence that fails to demonstrate convincingly the relationship between student learning style, preference for online instruction, and learning achievement. Therefore, using data from an introductory programming course that had both an online and a traditional section, the author examined the following research questions: 1. Is there a relationship between a student’s learning style and the selection of course delivery format (online or face to face)? 2. Is there a difference between the course grades of students based upon the course delivery format? 3. Is there a difference between the course grades of students based upon their learning style? 4. Is there an interaction between learning style and course delivery format based upon the course grades? Course content and organisation The present study was undertaken during the 2008 fall semester using as subjects 161 1st-year computer science majors, 77 (29 males and 48 females) of which were enrolled in the online section while the remaining 84 (33 males and 51 females) were enrolled in the face-to-face section of ‘Introduction to Programming Using Java—COMP120’. This is a beginning-level programming course aiming to teach students problem solving, algorithms and their design, and fundamental programming skills. This course is taught in the second semester of the 1st year and is appropriate for students with no prior programming experience. There are no strict prerequisites, but a basic background in math and computer skills is required. The Java language is used to introduce foundations of structured, procedural and object-oriented programming. Topics include I/O, data types, operators, operands, expressions, conditional statements, iteration, recursion, arrays, functions, parameter passing and returning values. Students are also introduced to classes, objects, object references, inheritance, polymorphism and exception handling. In its traditional format, the course is taught during a 12-week period with two 90-minute lectures and one 2-hour lab session each week. The two weekly lectures are in the traditional style with short presentations (less than 20 minutes long) which are based upon carefully prepared examples that illustrate key concepts, and in-class programming exercises which provide scaffolded practice opportunities for experimentation, feedback, and reflection. In each lab session, a sample project is given to students to run and see how things work, and then they are asked to modify the code and extend the functionalities of the program. The course website on Moodle LMS supplements classroom instruction by providing students with access to all lecture material, interactive textbook modules, laboratory modules, homework © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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assignments and solutions, self-diagnostic quizzes and old tests, study guides, and discussion boards. Using the online environment students can choose to view a recorded lecture or interact with a narrated Flash-based presentation, or read a step-by-step description of every activity that took place in class and run an interactive simulation. Self-check exercises focusing on specific language points are designed as true/false, multiple choice or fill-in-the-blank in order to engage students in active learning by practising the concepts they have learned in class. The students in the online group were taught by the same instructor, used the same online resources, covered the same lecture material, submitted the same homework and project assignments, and took the same exams as their on-campus counterparts. The only extra facility they had in their disposal was one instructor-led online session every 2nd week via Centra Live webconference system, in which they could see and hear the instructor commenting on their code and answering questions. Although both groups could participate in the classroom discussion lists and post their questions and answers, online students could not use face-to-face office hours to access the instructor. Instead, their office hours and lab support was facilitated by Moodle through email and group blogs. Procedure and instrumentation At registration time, all students were able to self-select to be in the traditional or in the online group of COMP120. Two weeks before the beginning of the semester, students in the online group had the opportunity to attend an intensive orientation course in campus laboratories that consisted of three lectures on the use of Moodle and Centra Live system. For every technical problem they faced during the course period, students could easily contact either the instructor or the university IT services. The Kolb Learning Styles Inventory (LSI), a statistically reliable and valid 12-item questionnaire, in which respondents attempted to describe their learning style, was administered online to both student groups 1 week after the start of the course. The format of the LSI is a forced-choice format that ranks an individual’s relative choice preferences among the four modes of the learning cycle: CE, RO, AC or AE. Each item in the LSI has four possible answers, and the respondents are asked to rank order these answers starting with a ‘4’ for the answer that best describes their learning preference down to a ‘1’ for the answer that seems the least like the way they would learn. This ranking produces a score for each of these learning orientations ranging from 12 to 48. Combining the scores of the four learning modes and following the formulas (AC)-(CE) and (AE)-(RO) results in two combination scores. By plotting the combination scores on a grid and identifying the quadrant where the two scores intersect, one can determine a specific learning style from among the four styles: Converger, Diverger, Accommodator and Assimilator. A correlational design was used to collect data and determine if there was a difference in learning styles of online versus on-campus students. The learning styles of the students were compared using a 2 (course delivery format) ¥ 4 (learning style) chi-square test of independence with an alpha level set at 0.05. Additionally, a 2 (online or traditional instruction) ¥ 4 (learning style) full factorial ANOVA was performed to see if a particular learning style could predict success (course grade) in the course. Course grade in this study was derived from a midterm exam, eight homework assignments, two group projects, and the final examination and could range between 0 and 100. Results The gender ¥ course delivery format cross-tabulation demonstrated that the association between course delivery format and gender is statistically insignificant at p < 0.001 (c2 = 0.146 with 1 degree of freedom). Descriptive statistics of the Kolb’s LSI and students’ course grades for both © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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Table 1: Descriptive statistics Dependent variable: grade Online
Converger Diverger Assimilator Accommodator Total
15 17 24 21 77
64.53 70.53 64.46 70.43 67.44
19.72 17.84 18.48 19.98 18.87
13 24 26 21 84
74.08 68.29 68.42 66.52 68.79
20.27 20.46 22.81 20.24 20.90
Table 2: Chi-square tests
Pearson chi-square Likelihood ratio Linear-by-linear association N of valid cases
Asymp. sig. (2-sided)
3.477† 3.509 0.027 161
3 3 1
0.324 0.320 0.868
†0 cells (0.0%) have expected count less than 5. The minimum expected count is 18.16.
delivery formats are shown in Table 1. From these data it is apparent that the predominant learning styles in the online group were the Assimilator (31%) and the Accommodator (27%), while in the traditional group most students fell into the Assimilator (31%) and Diverger (28%) learning styles. This result is in agreement with the findings of Federico (2000), who found that students with Assimilator and Accommodator learning style feel more comfortable taking online instruction, and Buerck et al (2003), who found that traditional students tend to be Assimilators. Students with the Divergent learning style performed the best in the online environment, in agreement with the findings of Wang, Wang, Wang and Huang (2006). While students with the Assimilator learning style were almost equally distributed between the online and on-campus groups, Divergers in the online group and Convergers in the face-to-face group performed the best. Accommodators in the online group performed as well as Divergers, while the traditional group performed the worst. To determine the answer to the first research question, a Pearson chi-square test was used to examine the relationship between student learning style and preference for online or face-to-face instruction. The results of this inferential test (Table 2) were nonsignificant, c2 (3, N = 161) = 3.477, p = 0.324, suggesting that students’ learning style did not influence their selection of taking instruction in either a face-to-face or fully online format. This finding is consistent with the results of the studies conducted by Oh and Lim (2005) and Brittan-Powell et al (2008). Based upon the results of students’ preferred learning style and their course achievement, a 2 (method of instruction) ¥ 4 (learning style) full factorial ANOVA (Table 3) was conducted to answer the next three research questions of this study. Homogeneity of variance was assumed, with a Levine’s statistic equal to 0.42. The second research question sought to determine if there was a difference in student achievement due to the instructional delivery method. Results revealed that students in the traditional © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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Table 3: Tests of between-subjects effects Dependent variable: grade Source Corrected model Instructional method Learning style Instructional method*Learning style Learning style
1243.179† 129.350 970.859
7 1 3
177.597 129.350 323.620
0.437 0.319 0.797
0.877 0.573 0.497
†R squared = 0.063 (adjusted R squared = 0.020).
group had higher (M = 68.79, SD = 20.90), but not significant higher performance than the online group (M = 67.44, SD = 18.87), based on their course grades, F(1, 153) = 0.319, p > 0.05. This finding is consistent with the results found by Terrell and Dringus (2000) and Buerck et al (2003), which suggest that there is no difference in students’ performance between face-to-face and online instruction. The third research question investigated whether students’ learning style influenced their academic performance (course grade) differentially contingent upon whether they took the course in either a face-to-face or a fully online delivery format. The 2 ¥ 4 ANOVA revealed no significant difference in students’ course grades between the online and face-to-face group, F(3, 153) = 0.797, p > 0.05. This result is in accordance with the findings of the research conducted by McNeal and Dwyer (1999) and Brittan-Powell et al (2008). The fourth research question aimed to find out if there was an interaction between instructional method and learning style, based upon the students’ course grades. The 2 ¥ 4 factorial analysis of variance in this case indicated no statistically significant interaction between the learning style and the method of instruction, based upon course grades, F(3, 153) = 0.205, p > 0.05. This result is in agreement with the findings of the research conducted by Aragon et al (2002), which suggest that there is no interaction between learning style and instructional method. Students can be equally successful in face-to-face and online environments, no matter what their learning style.
Discussion and implications for teaching practice As the Internet continues to become an integral part of life and education, the need to determine its effectiveness, as an instructional medium, to address diverse educational needs drives educators to examine learner variables that seem to influence student success in online environments. In this light, the present study compared an online course with an equivalent course taught in a traditional face-to-face format to investigate if students’ learning styles play a role in their decision to select an online or a face-to-face course format and in their achievement in these two learning environments. The results of the study are consistent with previous findings about the influence of learning style on the selection of instructional environment (online or face to face). As the chi-square test indicated that the learning styles of the online and traditional students had no significant difference, the reasons for students’ preference of one of the two course formats must be sought in other factors highlighted by related literature such as commitments outside university, technology competence and travel difficulties. Regarding the impact of learning style on student © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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achievement, a factorial analysis of variance showed that there was no significant difference between online and face-to-face learning environments. That means that learners can be just as successful in the online environment as they can in the face-to-face environment regardless of their learning style preferences. According to Johnson and Aragon (2003), ‘powerful online learning environments need to contain a combination of these principles: (1) address individual differences, (2) motivate the student, (3) avoid information overload, (4) create a real-life context, (5) encourage social interaction, (6) provide hands-on activities, and (7) encourage student reflection’. Appropriately designed educational materials, such as real-life examples, scenarios, conceptual models, and simulations, can support the delivery of experiential learning experiences and promote the construction of new meanings (Jonassen, 1999). Taking these recommendations into account, this study used a well-constructed course that offered an active learning environment with highly interactive components such as online exercises, lab activities, simulations and video, and a broad range of communication tools and found that differing learning styles can be accommodated successfully in both online and face-to-face learning environments. Because no significant difference was found in learning styles and learning outcomes between the two groups, it is self-evident that the learning activities used in the on-campus classroom had been also effective for the online classroom. With a subject unusual as programming, students are much more likely to adopt a range of learning approaches—from surface learning for syntax to deep learning for algorithms and applications—and engage in all manner of activities, trying to acquire knowledge of the basics and start writing programs (Jenkins, 2001). However, whatever the learning approach may be, the instructor must ensure that students engage in learning activities that promote the type of learning that is most appropriate to a particular learning task. Learning activities in COMP120 were designed to connect new concepts to previous ones by working on selected problems, using different representations of concepts and methods, and employing a number of learning approaches, such as analysis of each problem to its main components, design and comparison of solution paths, identification of similar problems, and modification and reuse of previous solutions. Combining delivery technologies with constructivist pedagogy, this course emphasised dialogue between students and created a collaborative climate that supported interactions, explanations, and reflections. With the instructor acting as a negotiator or facilitator of shared understandings, students felt free and comfortable to take control, collaborate, and perform complex tasks that required them to locate information that was presented in a variety of formats, such as text, image, simulation and video, and then interpret, organise, and share it in a meaningful way. Students with the Assimilator learning style prefer high instructor presence and in this course had the opportunity to be a part of everything during the learning process by listening to recorded lectures, reading online documents, watching lab demonstrations, following detailed directions and practising new concepts via active learning exercises. The tendency of Convergers to deal with technical problems and remain focused on task until completion was facilitated by a series of simulations, laboratory assignments, and practical applications of new concepts. Accommodators, who have the most hands-on approach and prefer to work in teams, had many sample projects to modify and see the outcome and two semester-long group projects to exchange information, take initiatives, and try different ways to achieve the common objective. As Divergers prefer to learn via logical instruction or hands-on exploration with conversations that lead to discovery, they had a variety of advance organisers and concrete examples to start from, and many programming assignments to use their imagination and instructor’s and peers’ feedback to solve problems. © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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The absence of a significant difference (t = 0.431; p = 0.667) among the scores of students in online (M = 67.44, SD = 18.87) and face-to-face (M = 68.79, SD = 20.90) sections is in accordance with the findings of previous studies arguing that there is ‘no significant difference’ between online learning and traditional learning in terms of student achievement, satisfaction, and overall course quality (Brittan-Powell et al, 2008; Russell, 2002). Although the 2 ¥ 4 ANOVA showed that online students had no significant difference in their learning achievement when compared with the traditional students based on their learning styles, there is some indication that Divergers and Accomodators are the greatest beneficiaries of the online environment and that Convergers perform better in the face-to-face setting. As Divergers and Accomodators share the same CE orientation, which focuses on being involved in experiences and dealing with immediate human situations in a personal way, it seems reasonable to assume that the broad opportunities for experimentation, collaboration, and communication offered by the online environment had an important impact on these two learning styles. The higher scores for the face-to-face Convergers could be accounted for by the fact that they ‘are drawn to and benefit from opportunities for guidance and feedback as they practice new skills or explore new knowledge’ (Fahy & Ally, 2005), preferring ‘public’ interaction (Atherton, 2002). Although research on learning styles and course design is not robust enough to provide course developers with standard guidelines, Currie (1995) advocates that instructors should utilize a variety of techniques and training aids and encourage an awareness of learning style and a broadening of the learner’s range of styles. Following Currie’s recommendations, instead of using Kolb’s LSI results to direct students to the learning resources that best suited their learning style preference, COMP120 materials were accessible to all students to study and then reflect on their own learning styles and work on strengthening their less preferred modes of learning. From students’ online discussions and suggestions it becomes clear that the integration of a variety of problem types in assignments that call for different skills can promote intellectual growth and adoption of a deep approach to learning. From the instructor’s viewpoint, the equivalence of student achievement levels in the two learning settings under study was mainly due to the fact that there was no differentiation in the learning styles of the participants or the learning activities they were engaged in. Providing learning materials that challenge and support students to develop deep levels of thinking and application, and integrating assessment practices in everyday teaching and learning, helps to create and maintain a learning environment both supportive and productive. Collaborative assignments, based on paired and small-group work, enhance interaction with instructors and other students and lead to high levels of mastery of course objectives. Online communication via discussion forums and blogs plays an essential role on the regulation of instructional pace by providing the means to gauge students’ reactions to instruction and review instructional goals and objectives throughout an instructional activity. Providing an appropriate instructional pace and personalised assistance during instructional activities ensures that the classroom climate remains positive, nurturing, and supportive for all types of learners, minimising the difference between online and on-campus students. Conclusion Internet technologies play an increasingly important role in higher education section, providing on-campus and web-based students the means to take control, perform tasks, and share successes. Dealing with diverse student populations engaged in higher education today, scholars continue to search for factors impacting academic achievement and predict success in face-toface and online learning environments. Although learning styles are considered by many to be one factor of success, there are still many controversies on this topic. This present study examined the issue of learning styles as a predictor of both preference for online or face-to-face instruction © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
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and course performance. The findings of this research indicate that learning style does not impact students’ choice for online or face-to-face instruction or their ability to successfully complete a course in any of these two instructional environments. These findings, however, should be further investigated using larger sample sizes, different courses of study, and possibly students randomly assigned to online and on-campus sections. Based on the results of this study, the implications for designing learning activities are clear. The online classroom, either as a sole instructional channel or as a complement for on-campus teaching and learning, provides the tools to address different learning styles and preferences. Students in the online or face-to-face classroom do not differ in the way they process information, so the same learning activities could be designed and utilised in both environments. Understanding their learning styles, students can effectively choose the tools that will add the most value to the learning experience. Being aware of their students’ learning styles, instructors can design online modules and activities, or redesign sequences of events and interventions, to accommodate effectively their different academic skills and interests. References Aragon, S. R., Johnson, S. D. & Shaik, N. (2002). The influence of learning style preferences on student success in online versus face-to-face environments. American Journal of Distance Education, 16, 4, 227– 244. Atherton, J. S. (2002). Learning and teaching: learning from experience. Retrieved October 26 2009, from http://www.learningandteaching.info/learning/experience.htm Battalio, J. (2009). Success in distance education: do learning styles and multiple formats matter? American Journal of Distance Education, 23, 2, 71–87. Benham, C. H. (2002). Training effectiveness, on-line delivery, and the influence of learning style. Proceedings of the 2002 ACM Special Interest Group on Computer Personnel Research Conference, 41–46 May. Kristiansand, Norway. Bonk, C. J., Wisher, R. A. & Lee, J. (2003). Moderating learner-centered e-learning: problems and solutions, benefits and implications. In T. S. Roberts (Ed.), Online collaborative learning: theory and practice (pp. 54–85). Hershey, PA: Idea Group Publishing. Boyd, D. (2004). The characteristics of successful online students. New Horizons in Adult Education, 18, 2, 31–39. Brittan-Powell, C., Legum, H. & Taylor, E. (2008). The relationship between student learning style, selection of course delivery format, and academic performance. International Journal of Instructional Technology and Distance Learning, 5, 5, 41–46. Buerck, J. P., Malmstrom, T. & Peppers, E. (2003). Learning environments and learning styles: nontraditional student enrollment and success in an Internet-based versus a lecture-based computer science course. Learning Environments Research, 6, 2, 137–155. Byrne, P. & Lyons, G. (2001). The effect of student attributes on success in programming. ACM SIGCSE Bulletin, 33, 3, 49–52. Currie, G. (1995). Learning theory and the design of training in a health authority. Health Manpower Management, 21, 2, 13–19. Diaz, D. & Cartnal, R. (1999). Students’ learning styles in two classes: online distance learning and equivalent on-campus. College Teaching, 47, 4, 130–135. Ellis, R. D. & Kurniawan, S. H. (2000). Increasing the usability of online information for older users—a case study in participatory design. Instructional Journal of Human-Computer Interaction, 12, 2, 263–276. Fahy, J. & Ally, M. (2005). Student learning style and asynchronus computer-mediated conferencing (CMC) interaction. The American Journal of Distance Education, 19, 1, 5–22. Federico, P. (2000). Learning styles and student attitudes toward various aspects of network-based instruction. Computers in Human Behavior, 16, 4, 359–379. Felder, R. M. & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78, 674–681. Gilbert, J. E. & Han, C. Y. (1999). Adapting instruction in search of a significant difference. Journal of Network and Computing Applications, 22, 3, 149–160. Goold, A. & Rimmer, R. (2000). Factors affecting performance in first year computing. ACM CIGCSE Bulletin, 32, 2, 39–43. © 2010 The Author. British Journal of Educational Technology © 2010 Becta.
British Journal of Educational Technology
Vol 42 No 5 2011
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