Effects of Inquiry-Based Science Instruction on Science Achievement and Interest in Science

The Journal of Educational Research, 105:134–146, 2012 C Taylor & Francis Group, LLC Copyright ISSN: 0022-0671 print / 1940-0675 online DOI:10.1080/00220671.2010.533717

Effects of Inquiry-Based Science Instruction on Science Achievement and Interest in Science: Evidence from Qatar SHALJAN AREEPATTAMANNIL Nanyang Technological University, Singapore

ABSTRACT. The author sought to investigate the effects of inquiry-based science instruction on science achievement and interest in science of 5,120 adolescents from 85 schools in Qatar. Results of hierarchical linear modeling analyses revealed the substantial positive effects of science teaching and learning with a focus on model or applications and interactive science teaching and learning on science achievement and interest in science. In contrast, science teaching and learning using student investigations and hands-on activities had substantial negative effects on science achievement in the context of other variables. Implications of the findings for educational policy and classroom practice are discussed.

Keywords: adolescents, hierarchical linear modeling, inquirybased science instruction, interest in science, science achievement

T

he Arabian Gulf nation of Qatar is one of the wealthiest countries in the world (World Economic Forum, 2009). Although Qatar chose to invest its wealth in education, “the education system for kindergarten through grade 12 (K–12) does not adequately prepare Qataris for work or post-secondary study” (Stasz et al., 2007, p. 13). The elementary and secondary education system in Qatar is widely recognized as “rigid, outmoded, and resistant to reform” (Brewer et al., 2007, p. 17). Moreover, curriculum and instruction in schools across Qatar emphasize rote memorization (Zellman et al., 2009). The dismal performance of Qatari adolescents on the Programme for International Student Assessment (PISA) 2006 science assessment attests to the aforementioned findings. Adolescents from non-Organization for Economic Cooperation and Development (OECD) countries—Qatar and Kyrgyzstan—were among the lowest performing adolescents in PISA 2006 (OECD, 2007). Qatari adolescents performed significantly below the OECD average (OECD, 2007; Supreme Council of Education [SCE], 2007). OECD (2004) posited,

In an increasingly technological world, literacy is not just about reading, but citizens also need to be scientifically literate. Scientific literacy is important for understanding environmental, medical, economic, and other issues that confront modern societies, which rely heavily on technological and scientific advances. Further, the performance of a country’s best students in scientific subjects may have implications for the part which that country will play in tomorrow’s advanced technology sector, and for its general international competitiveness. Conversely, deficiencies in mathematical and scientific literacy can have negative consequences for individuals’ labour-market and earnings prospects and for their capacity to participate fully in society. (p. 299)

Given the serious flaws in the present education system in Qatar and its failure in adequately preparing Qataris for the challenges of today’s technology-based societies, there is a dire need to carefully and critically examine the factors influencing science achievement and interest in science of adolescents in Qatar. Further, Qatar joined PISA in 2006 (OECD, 2007), and there is relatively little research that examined the academic trajectories of adolescent students in schools across Qatar. No study to date has examined the effects of inquiry-based science instruction on science achievement and interest in science for adolescent students in Qatar. Considering the paucity of research, more theoretically and methodologically diverse empirical research is needed to construct a sophisticated understanding of the influence of classroom instructional strategies on scientific literacy and interest in scientific subjects for adolescent students in the Qatari school setting.

Furthermore, because “enhancement and propagation of scientific inquiry is one of the core elements of scientific education” (Hanauer, Hatfull, & Jacobs-Sera, 2009, p. 11), there is a growing consensus among science experts that Address correspondence to Shaljan Areepattamannil, Centre for International Comparative Studies, National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637 616. (E-mail: shaljan.a@nie.edu.sg)

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instructional contexts that stimulate students’ motivation to inquire and learn, enabling them to use cognitive and metacognitive strategies, are vital to enhance students’ scientific inquiry skills and scientific literacy (e.g., Yoon, 2009).

Therefore, this study adds to the growing body of research (e.g., McNeill & Pimentel, 2009) on studying the effects of inquiry-based science instruction on scientific literacy and interest in science for adolescent students. Using hierarchical linear modeling (HLM; Raudenbush & Bryk, 2002) as an analytic strategy, in the present study I focused on the following research questions: Research Question 1: How much of the variation in science achievement and interest in science is within and between schools in Qatar?

Research Question 2: What are the effects of inquiry-based science instruction on science achievement and interest in science for adolescents in Qatar?

Inquiry-Based Science Instruction, Science Achievement, and Interest in Science A notable decline in student enrollments and interests in science at high school and university levels have been reported in several OECD countries, such as Australia (Dekkers & De Laeter, 2001; Goodrum, Hackling, & Rennie, 2001), Canada (Statistics Canada, 2009), France (Lyons, 2006), Germany (Haas, 2005), United Kingdom (Murphy & Beggs, 2003), and the United States (Lyons, 2006). Hence numerous educational policy doctrines in recent years have advocated for inquiry-based science or scientific inquiry as a panacea for the present crisis in science education (e.g., Goodrum et al., 2001; Millar & Osborne, 1998; National Research Council [NRC], 1996, 2000, 2005, 2007). The definition of scientific inquiry, however, has to a certain extent been quite elusive, and this term has been described and defined in a variety of ways (Hanauer et al., 2009). The National Science Education Standards (NSES) defined scientific inquiry as [a] multifaceted activity that involves making observations; posing questions; examining books and other sources of information to see what is already known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, explanations, and predictions; and communicating results. Inquiry requires identification of assumptions, use of critical and logical thinking, and consideration of alternative explanations. (NRC, 1996, p. 23)

More recently, with the inclusion of varied laboratory experiences, the NRC (2005) has expanded their description of what constitutes scientific inquiry activities: “Laboratory experiences provide opportunities for students to interact directly with the material world (or with data drawn from the material world), using the tools, data collection techniques, models, and theories of science” (p. 3). Thus, scientific inquiry covers a wide range of diverse activities—studentcentered interactions, student investigations and hands-on

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activities, and focus on models or applications in science—to foster students’ interest to learn science and to enhance their scientific literacy. Mounting evidence demonstrates the positive effects of inquiry-based science teaching and learning (e.g., Lee, Hart, Cuevas, & Enders, 2004; McNeill & Pimentel, 2009; Wu & Hsieh, 2006). Therefore, policy doctrines (e.g., NRC, 1996, 2005) and science teachers’ associations (e.g., National Science Teachers Association [NSTA], 2004) recommended science teachers to integrate scientific inquiry into the teaching and learning of science.

Student-centered interactions are the sine qua non of inquiry-based science teaching and learning (Mercer, Dawes, Wegerif, & Sams, 2004; Olitsky, 2007; Stamovlasis, Dimos, & Tsaparlis, 2006). Traditional classroom discourse patterns—authoritative classroom discourse—wherein the teacher dominates and controls the talk would hinder the social construction of knowledge in science classrooms by drastically minimizing student-centered interactions (McNeill, 2009; McNeill & Pimentel, 2009). Hence authoritative classroom discourse patterns would be detrimental to equipping students with the skills and habits of mind required for living in the 21st century (Wolfe & Alexander, 2008). Promotion of higher order thinking and learning as well as intellectual development requires students’ active engagement in pedagogic activity through discussion, dialogue, and argumentation (e.g., Alexander, 2008; Gillies, 2008; Gillies & Khan, 2008; Mercer & Littleton, 2007; Wolfe & Alexander).

Therefore, encouraging dialogic classroom discourse patterns (see Mortimer & Scott, 2003; Wells, 1999) in which students engage in discussions and tasks that apprentice them into the discourse and methodology of science would promote students’ engagement in science and their science achievement (e.g., Kalu, 2008; Kalu & Ali, 2004; Olitsky, 2007; Stamovlasis et al., 2006; Zady, Portes, & Oches, 2003).

Indeed, “classroom environments that promote learning are thus those in which the teacher’s role is one of facilitator of learning and co-constructor of new information through joint negotiation rather than a transmitter of given information” (Sharpe, 2008, p. 133). Scott, Mortimer, and Aguiar (2006), however, postulated that science lessons must entail authoritative and dialogic passages of interaction for meaningful learning to result from a sequence of science teaching. Nevertheless, students engage more effectively in classroom discourse when they are explicitly taught how to engage critically and constructively with each other’s ideas, challenge and counterchallenge proposals, and discuss alternative propositions before reaching agreement are important if students are to talk and reason effectively together (e.g., Gillies & Boyle, 2008; Rojas-Drummond & Mercer, 2003). Further, it is also important to train teachers to use a repertoire of instructional techniques, strategies, and approaches “that challenge children’s cognitive and metacognitive thinking and promote learning” (Gillies & Khan, 2008, p. 338).

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Student investigations and hands-on activities also play a distinctive role in inquiry-based science teaching and learning (Hofstein, Kipnis, & Kind, 2008; Mintzes, Wandersee, & Novak, 2005). A plethora of studies document that student investigations and hands-on science activities have the potential to enhance students’ higher order learning skills, such as metacognition and argumentation (e.g., Dori & Sasson, 2008; Dori, Sasson, Kaberman, & Herscovitz, 2004; Kaberman & Dori, 2009; Kipnis & Hofstein, 2008). Furthermore, incorporating student investigations and hands-on activities into science teaching and learning would help in developing students’ positive attitudes toward science and sustaining their motivation (Abd-El-Khalick et al., 2004; Hofstein & Mamlok-Naaman, 2007). Therefore, students exposed to hands-on science instruction frequently get significantly higher scores in science than those students who experienced hands-on science infrequently (e.g., Jaakkola & Nurmi, 2008; Klahr, Triona, & Williams, 2007).

Finally, modeling-centered inquiry—a core scientific practice—is also central to inquiry-based science teaching and learning (see Matthews, 2007; Schwarz, 2009; Schwarz et al., 2009; Shen & Confrey, 2007). Scientific modeling is a higher order process skill (Akerson et al., 2009) in which learners engage in scientific inquiry whose focus is on the creation, evaluation, and revision of scientific models that can be applied to understand and predict the natural world (Schwarz, 2009; Schwarz et al., 2009; Windschitl & Thompson, 2006). Scientific models include “scaled and exaggerated objects; symbols, equations and graphs; diagrams and maps; and simulations that facilitate scientific communication. They can be concrete, abstract, or theoretical depending on the needs . . . but above all models must enhance investigation, understanding, and communication” (Harrison & Treagust, 2000, p. 1012).

A growing body of research suggests that engaging learners in modeling-centered inquiry can help them build subject matter expertise (e.g., Besson & Viennot, 2004; Kenyon, Schwarz, & Hug, 2008), epistemological understanding (i.e., understanding the nature of science; e.g., Kenyon et al., 2008; Lesh & Doerr, 2003), expertise in the practices of building and evaluating scientific knowledge (e.g., Stewart, Cartier, & Passmore, 2005), and develop their scientific literacy (e.g., Bunce & Gabel, 2002; Schwarz, 2009).

Nonetheless, in order for teachers to successfully implement model-centered instruction in their classrooms, they must have a clear understanding of what it entails (Harlow, 2010; Windschitl, Thompson, & Braaten, 2008).

In short, the burgeoning literature pertaining to inquirybased science teaching and learning highlight the positive effects of inquiry-based science instruction on science achievement and interest to learn science. Indeed, students excel academically in a learner-centered, constructivist learning environment in which the construction of knowledge is interactive, inductive, and collaborative (Ozkal, Tekkaya, Cakiroglu, & Sungur, 2009). Student investigations and hands-on activities have the “potential to enhance students’

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conceptual and procedural understanding, their practical and intellectual skills, and their understanding of the nature of science” (Hofstein et al., 2008, p. 59). Similarly, a scientific modeling approach allows a wide range of students to meaningfully participate in science (Kenyon et al., 2008).

Undoubtedly, scientific inquiry engages learners deeply with content and with the epistemic practices of authentic science (Windschitl et al., 2008). Thus the use of scientific inquiry is crucial to scientific literacy. Given the disappointing performance of adolescent students in Qatar on the PISA 2006 science assessment, it is critical to examine the effects of inquiry-based science instruction on these adolescents’ science achievement and interest in science.

Method Sample Data for the present study were drawn from the Qatari sample of the PISA 2006 that contained 6,265 fifteen-year old students in Grades 7–12 from 131 schools. The sample consisted of 2,273 immigrant children (1,143 boys, 1,130 girls) and 3,445 nonimmigrant children (1,527 boys, 1,918 girls). Another 547 (9%) students did not have information on their immigration status. The number of students per school in the PISA 2006 Qatar sample varied between 1 and 188. Because the number of students per school affects the statistical power of a cluster randomized trial, schools with at least 15 students were chosen to reduce bias and increase efficiency (see Spybrook, 2008), resulting in a final sample of 5,120 students (2,222 boys, 2,898 girls; 2,048 immigrant, 3,072 nonimmigrant) nested within 85 schools (61 rural, 24 urban; M school size = 60).

Variables Outcome variables. As scientific literacy was the main focus of PISA 2006, in this study I focused on the effects of inquiry-based science on science achievement and interest in science. “Scientific literacy relates to the ability to think scientifically and to use scientific knowledge and processes to both understand the world around us and to participate in decisions that affect it” (Thomson & De Bortoli, 2008, p.

2). The PISA 2006 science assessment provided scores on a combined scientific literacy scale (i.e., five plausible value [PV] estimates in science for each student), which consisted of all items in the three subscales—Identifying Scientific Issues, Explaining Phenomena Scientifically, and Using Scientific Evidence (see OECD, 2009b). In addition to the combined scientific literacy scale, PISA 2006 provided five PV estimates in interest in science for each student. Therefore, science achievement (scientific literacy) and interest in science were the dependent variables in analyses. Each of these scales had a mean of 500 and a standard deviation of 100 (OECD, 2007). The PVs feature of HLM 6.08 for Windows (Raudenbush, Bryk, Cheong, Congdon, & du

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Toit, 2004) was used, prompting the program to run models for each of the five PVs internally, producing their average value and correct standard errors.

Student- and school-level variables. All student- and school-level variables reported in the study were obtained or computed from the PISA 2006 data. Because the PISA sample is not class based, PISA 2006 did not collect data at the classroom–teacher level (see OECD, 2009a). However, in educational research students are generally asked to evaluate features of their lessons to assess the characteristics of the learning environment (L¨udtke, Robitzsch, Trautwein, & Kunter, 2009; L¨udtke, Trautwein, Kunter, & Baumert, 2006). As student reports are often more easily obtained than reports from teachers (L¨udtke et al., 2009), there has been a consistent increase in the use of student reports to assess the characteristics of the learning environment (e.g., Frenzel, Pekrun, & Goetz, 2007; Friedel, Cortina, Turner, & Midgley, 2007; Kunter, Baumert, & K¨oller, 2007; Trautwein & L¨udtke, 2009). In the PISA 2006 science assessment, 17 items asked students how often specific teaching activities pertaining to scientific inquiry occurred in regular science lessons. The PISA 2006 used 15 of these items to construct four individual-level scales. Because these four scales had acceptable internal consistency, they were used in statistical analyses to examine the effects of inquiry-based science instruction on science achievement and interest in science.

Further, these student-level items were not aggregated to the school level because generalizing a relationship that was found on an individual level to the aggregate level may pose methodological challenges (see Bliese, 2000). More specifically, such generalizations may result in reverse ecological fallacy or atomistic fallacy (see Hofstede, 2001).

All items were measured on a 4-point Likert-type scale ranging from 1 (never or hardly ever) to 4 (in all lessons).

The science teaching and learning with a focus on model or applications scale (4 items; Cronbach’s α = .79) measured the frequency with which science lessons were taught with a focus on learning about science as a model and science in real-life applications (e.g., “The teacher uses examples of technological application to show how school science is relevant to society”). The science teaching and learning using student investigations scale (3 items; Cronbach’s α = .77) measured the frequency with which student investigations occurred in regular science lessons (e.g., “Students are asked to do an investigation to test out their own ideas”).

The science teaching and learning using hands-on activities scale (4 items; Cronbach’s α = .81) measured the frequency with which hands-on activities occurred in regular science lessons (e.g., “Students do experiments by following the instructions of the teacher”). The interactive science teaching and learning scale (4 items; Cronbach’s α = .78) measured the frequency with which interactive teaching activities occurred in regular science lessons (e.g., “The lessons involve students’ opinions about the topics”).

Student and school demographic characteristics have been shown to be important predictors of academic achieve-

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ment in prior research (e.g., Caro, McDonald, & Willms, 2009; Laurie, 2009; McBride, Dyer, Liu, Brown, & Hong, 2009; Schoon, Parsons, & Sacker, 2004). Hence in the present study I used control measures to control for potential specification errors in estimating the effects of inquiry-based science on science achievement and interest in science. The student demographic characteristics taken into account were gender (male = 0, female = 1), immigrant status (nonimmigrant = 0, immigrant = 1), and an index derived from student responses on parental occupation, namely, the highest occupational status of parents based on the International Socioeconomic Index of Occupational Status (HISEI). In PISA 2006, data for mother’s and father’s occupations were collected. The index captures the attributes of occupations that convert parents’ education into income and expresses the higher value of either the mother’s or the father’s occupational level (OECD, 2007). The school demographic characteristics taken into account were the average value for HISEI per school, school location (rural = 0, urban = 1), and school enrollment size. Parents’ occupation (HISEI), school average parents’ occupation, and school enrollment size were used as continuous variables in analyses.

Missing data. Given that more than 5% of the studentand school-level cases had one or more missing values, I employed multiple imputation (MI; Little & Rubin, 2002; Rubin, 1987; Schafer, 1997) to avoid the underestimation of standard errors (Newman, 2003; Peugh & Enders, 2004).

Ten independent imputed data sets were created for each level using the PASW Missing Values’ multiple imputation procedure (SPSS for Windows, Version 18). Separate analyses were conducted for each imputed data set. The parameter estimates across the analyses were averaged and standard errors were combined, taking into account the variance in the parameter within each analysis and the variability between imputed data sets (see Schafer & Olsen, 1998).

Statistical Analyses Hierarchical linear modeling. Given the hierarchical structure of the PISA 2006 dataset (i.e., students nested within schools), multilevel analyses were conducted using HLM 6.08 for Windows (Raudenbush et al., 2004). The randomintercepts model with fixed slopes (Raudenbush & Bryk, 2002) was employed (see Appendix). Dichotomous variables were retained in their original metric. All continuous student- and school-level variables were centered on the grand mean. Normalized sampling weights for students and schools were employed in HLM analyses to make the sample reflective of the population (see OECD, 2009a). For all analyses, the solutions were generated on the basis of full maximum likelihood estimation (FIML). Further, all HLM analyses were first conducted with the 10 complete data sets, and the integrated results are reported (see Raudenbush & Bryk, 2002).

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The model building followed a step-up strategy as suggested by Raudenbush and Bryk (2002). At the first stage, a fully unconditional model (or null or intercept-only model), containing only an outcome variable and no independent variables, was built. The intercept-only model is equivalent to a one-way random-effects analysis of variance (Raudenbush & Bryk, 2002). The intercept-only models were used to identify the sources of variations within the two outcome variables—science achievement and interest in science—by partitioning the total variance in the outcome variables into their within-school (level 1) and between-school (level 2) components. At the second stage, student-level variables were added to the fully unconditional models to examine the statistical significance of student-level predictors. The statistically significant student-level variables were entered into the level 1 model. At the final stage, the statistical significance of school-level predictors was examined by employing the level 2 exploratory analysis subroutine available in HLM 6.08. The statistically significant school-level variables were entered into the level 2 model. The proportion of reduction in variance as accounted for by the models served as a basis for making a judgment about the relative importance of student- and school-level variables (Raudenbush & Bryk, 2002). As recommended by Hox (2002), a variable was considered to have a small effect if it explained 1% variance, a medium effect if it explained 10% variance, and a large effect if it explained 25% variance.

Results Descriptive Statistics and Zero-Order Correlations Descriptive statistics for the student- and school-level variables are presented in Table 1. The student-level mea-

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sures were positively and significantly correlated to a moderate degree (Tabachnick & Fidell, 2007). The highest positive correlation was between interactive science teaching and learning and science teaching and learning using handson activities (r = .73, p < .01), followed by science teaching and learning using student investigations and science teaching and learning using hands-on activities (r = .72, p < .01), interactive science teaching and learning and science teaching and learning with a focus on models or applications (r = .71, p < .01), science teaching and learning with a focus on models or applications and science teaching and learning using student investigations (r = .70, p < .01), science teaching and learning with a focus on models or applications and science teaching and learning using hands-on activities (r = .69, p < .01), and interactive science teaching and learning and science teaching and learning using student investigations (r = .65, p < .01).

Partitioning of Variance in Science Achievement and Interest in Science To address the first research question, two separate fully unconditional models were fitted for the two outcome variables—science achievement and interest in science (see Tables 2 and 3). These intercept-only models revealed that the average science achievement (γˆ 00 = 358.67) and the average interest in science (γˆ 00 = 567.55) varied across schools in Qatar, χ 2(84, N = 5,120) = 4287.64, p < .001; χ 2(84, N = 5,120) = 489.10, p < .001, respectively. Given that the OECD average of all participating countries is 500 with a standard deviation of 100, Qatar performed significantly below the OECD average in scientific literacy (358.67 ± 1.96 [7.52] = 343.93, 373.41). In contrast, Qatar scored above

TABLE 1. Descriptive and Distributional Statistics, and Cronbach’s Alphas Variable Level 1 Student demographics Gender Immigrant status Parents’ occupation Inquiry-based science Science teaching/learning with a focus on models or applications Science teaching/learning using student investigations Science teaching/learning using hands-on activities Interactive science teaching/learning Level 2 School demographics School location School size School average parents’ occupation Note. n students = 5,120; n schools = 85.

M

SD

Skewness

Kurtosis

Cronbach’s α

0.57 0.40 61.62

0.50 0.49 13.16

−0.10 0.45 −0.53

−1.99 −1.79 0.32

— — —

2.65 2.42 2.54 2.76

0.79 0.87 0.79 0.79

−0.20 0.02 −0.15 −0.31

−0.61 −0.93 −0.60 −0.59

.79 .77 .81 .78

0.28 393.79 61.10

0.45 288.62 3.96

1.00 0.95 −0.71

−1.02 0.65 1.76

— — —

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Coefficient

1.76 2.66 1.71

−17.67∗∗∗ −8.44∗∗ 8.36∗∗∗

10

2.49 0.08 2.13

31.21∗∗∗ 0.21∗ 10.93∗∗∗

NA

6.47

SE

344.77∗∗∗

Coefficient

3391.12 3453.59 .49 24

[343.93, 373.41]

95% CI

Level 1 model

4470.77 3833.25 .54 NA

7.52

SE

Note. n students = 5,120; n schools = 85. CI = confidence interval. p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Intercept variance (τˆ 00 ) Level 1 variance (σˆ 2 ) ˆ Intraclass correlation (ρ) Variance in achievement between schools explained (%) Variance in achievement within schools explained (%)

Intercept 358.67∗∗∗ Level 1 Immigrant status Parents’ occupation Science teaching or learning with a focus on models or applications Science teaching or learning using student investigations Science teaching or learning using hands-on activities Interactive science teaching/ or earning Level 2 School average parents’ occupation

Variable

Null model

31.12∗∗∗ 0.19∗ 10.90∗∗∗

344.29∗∗∗

Coefficient

[5.01, 11.71]

[–13.65, –3.23]

1.64

6.47∗∗∗

10

2739.71 3453.63 .44 39

1.71

2.66

1.76

2.48 0.08 2.13

5.81

SE

95% CI

[3.25, 9.68]

[5.03, 11.73]

[−13.63, 3.21]

[−21.12, 14.22]

[26.26, 35.98] [0.03, 0.34] [6.72, 15.07]

[332.90, 355.67]

Final model

8.38∗∗∗

−8.42∗∗

[–21.11, –14.22] −17.67∗∗∗

[26.32, 36.09] [0.05, 0.36] [6.75, 15.10]

[332.09, 357.45]

95% CI

TABLE 2. Fixed Effects Estimates and Variance–Covariance Estimates for Models of the Predictors of Science Achievement

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Note. n students = 5,120; n schools = 85. CI = confidence interval. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Intercept variance (τˆ 00 ) Level 1 variance (σˆ 2 ) ˆ Intraclass correlation (ρ) Variance in interest between schools explained (%) Variance in interest within schools explained (%)

567.55∗∗∗

Intercept Level 1 Immigrant status Science teaching or learning with a focus on models or applications Interactive science teaching or learning Level 2 School average parents’ occupation

6

NA

3.27

13.97∗∗

917.88 10127.44 .08 4

3.40 3.31

16.63∗∗∗ 20.60∗∗∗

SE 3.99

Coefficient

[559.94, 575.15] 559.60∗∗∗

95% CI

95% CI

[7.56, 20.38]

[9.97, 23.29] [14.11, 27.09]

[551.78, 567.42]

Level 1 model

958.44 10802.93 .08 NA

3.88

Coefficient SE

Variable

Null model

TABLE 3. Fixed Effects Estimates and Variance–Covariance Estimates for Models of the Predictors of Interest in Science

0.94

−3.75∗∗∗

6

790.55 10129.61 .07 17

3.27

3.36 3.31

3.69

SE

13.80∗∗∗

17.40∗∗ 20.65∗∗∗

560.03∗∗∗

Coefficient

95% CI

[−5.59, 1.91]

[7.39, 20.21]

[10.81, 23.98] [14.16, 27.14]

[552.79, 567.26]

Final model

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the OECD average in terms of interest in science (567.55 ± 1.96 [3.88] = 559.94, 575.15).

The models yielded a reliability of λˆ = 0.98 and λˆ = 0.79, respectively, indicating that the sample means tend to be a reliable indicator of true school means. Whereas 54% of the variance in science achievement was between schools (ρˆ = 0.54) and 46% of the variance involved students within schools in Qatar, only 8% of the variance in interest in science was between schools (ρˆ = 0.08) and 92% of the variance involved students within schools in Qatar.

Predicting Science Achievement and Interest in Science To answer the second research question, separate level 1 models were estimated for the outcome variables by adding student-level predictors to the intercept-only models, but without entering predictors at the other level (i.e., school) of the hierarchy. At this stage, the study followed a step-up strategy (Raudenbush & Bryk, 2002) to examine which of the seven student-level predictors had a statistically significant (p < .05) effect on the outcome variables—science achievement and interest in science. Although six studentlevel variables had a statistically significant effect on science achievement, only three student-level variables had a statistically significant effect on interest in science (see Tables 2 and 3). Hence these variables were included in the model at this stage. At the next stage, a level 2 model was estimated by adding school-level predictors into the model using the step-up strategy. To examine the potentially statistically significant school-level predictors, the level 2 exploratory analysis subroutine available in HLM 6.08 was used. Only one school-level variable—school average parents’ occupation—was found to be statistically significant for science achievement and interest in science. Therefore, school average parents’ occupation was included in the final models for both science achievement and interest in science.

The level 1 and final models indicated that immigrant status had a substantial positive effect on science achievement as well as on interest in science, suggesting that immigrant adolescents in Qatar have higher levels of scientific literacy and interest in science than their nonimmigrant counterparts. However, parents’ occupation was a statistically significant positive predictor of science achievement alone. Whereas the school average parents’ occupation had a positive effect on science achievement, it had a negative effect on interest in science.

Science teaching and learning with a focus on models or applications and interactive science teaching and learning were statistically significant positive predictors of science achievement and interest in science. On average, adolescents who reported that their teachers focused on models or applications in science lessons and employed interactive teaching and learning in science lessons scored higher than those adolescents who reported that their teachers did not employ either of these instructional strategies in science lessons. In contrast, science teaching and learning using stu-

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dent investigations and science teaching and learning using hands-on activities were statistically significant negative predictors of science achievement.

Although the addition of student-level variables to the level 1 model explained 24% of the between-school variance and 10% of the within-school variance for science achievement, the level 1 model explained only 4% of the between-school variance and 6% of the within-school variance for interest in science. For science achievement, the percentage of variance between schools (i.e., intraclass correlation coefficient [ICC]) was reduced from 54% in the null model to 44% in the final model. However, the percentage of variance between schools (ICC) was reduced from 8% in the null model to only 7% in the final model for interest in science. Further, the final model for science achievement explained 39% of the variance between schools and 10% of the variance within schools, and the final model for interest in science explained 17% of the variance between schools and 6% of the variance within schools. The reliability of the final models were λˆ = 0.97 and λˆ = 0.77 for science achievement and interest in science, respectively.

Discussion The purpose of the study was to examine the effects of inquiry-based science instruction on science achievement and interest in science for adolescents in Qatar. Despite differences in the measures of instruction, consistent with previous research (e.g., Lee et al., 2004; Wu & Hsieh, 2006), the results of the study indicated that inquiry-based science instruction had an effect on science achievement as well as on interest in science. Nevertheless, there were large amounts of variance left unexplained in the specified models for science achievement and interest in science, suggesting that there are other important between and within school factors influencing science achievement and interest in science of adolescents in Qatar, which were not explored in the present study. Despite these adolescents’ high level of interest in science, their comparatively low science performance scores warrant further research to investigate the influence of other pertinent student, class or teacher, and school level variables on scientific literacy.

Although the science performance of immigrant adolescents in Qatar was significantly below the OECD average, they outperformed their nonimmigrant peers. This finding may appear surprising in light of the findings of a large number of studies (e.g., Schnepf, 2007) that highlight the performance disadvantage of immigrant students across countries. This finding suggests that immigrant adolescents in Qatar may have stronger learning dispositions than their nonimmigrant peers. Previous studies (e.g., OECD, 2006) indicate that immigrant students tend to report similar or even higher levels of positive learning dispositions compared with their nonimmigrant counterparts. Moreover, immigrant students often report higher level of interest and motivation in school subjects and more positive attitudes

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toward schooling (OECD, 2006). Given the low performance of nonimmigrant adolescents in comparison with the their immigrant peers, it is of increasing importance for educators in Qatar to understand how to target the educational needs of the native-born population so that they do not fall behind immigrant students. Targeting science literacy in the early school years may help to develop feelings of competence and to sustain an enduring interest in learning about science (e.g., Anderson & Helms, 2001; Mantzicopoulos, Patrick, & Samarapungavan, 2008; Rennie, Feher, Dierking, & Falk, 2003).

In congruence with the findings of previous research (e.g., Schoon et al., 2004), proxy measures of socioeconomic status (SES)—parents’ occupation and school average parents’ occupation—were positively associated with science achievement of adolescents in Qatar. This finding indicates that adolescents from higher socioeconomic backgrounds tend to have higher science achievement than their peers from lower socioeconomic backgrounds. Furthermore, adolescents who attend relatively advantaged schools tend to have higher science achievement than their counterparts who attend relatively disadvantaged schools. In other words, adolescents from families with a lower SES in Qatar may be attending overcrowded, inner-city schools with limited resources. Such schools may lack the equipment and resources to offer scientific inquiry experiences to their students. Therefore, the findings of the study underline the need to provide the less privileged students and the disadvantaged schools with appropriate support, which, in turn, may help the less privileged students to succeed academically.

Another significant finding from the study indicated that science teaching and learning with a focus on models or applications had substantial positive effects on science achievement and interest in science. This finding is consistent with the findings of previous research (e.g., Bunce & Gabel, 2002; Kenyon et al., 2008). Indeed, “teachers need to know, among other things, about children’s ideas about models, how children develop models, and methods of best practices for facilitating children’s understanding of how models are used in science” (Harlow, 2010, p. 4). The quality of teachers and teaching are among the most important factors shaping the learning and growth of students across the educational systems of the world (e.g., Darling-Hammond & Ball, 1998; Ingersoll, 2007; Rosenberg, Heck, & Banilower, 2005). Hence increasing the number of teachers in schools across Qatar who are confident and willing to engage in model-based inquiry in their classrooms may not only enhance students’ development of conceptual science content knowledge but also facilitate students’ acquisition of critical scientific thinking skills.

Contrary to what might be expected, science teaching and learning using student investigations and hands-on activities had negative effects on science achievement of adolescents in Qatar. Given the extensive volume of research that documents the positive effects of student investigations and

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hands-on activities on science achievement (e.g., Jaakkola & Nurmi, 2008; Klahr et al., 2007), this finding is counterintuitive. Nonetheless, science teaching and learning using student investigations was also found to be a strong negative predictor of science performance among Canadian (Areepattamannil, Freeman, & Klinger, 2011) and Finnish (Lavonen & Laaksonen, 2009) adolescents. Future researchers need to continue to investigate the influence of student investigations and hands-on activities on science achievement.

In contrast, interactive science teaching and learning had positive effects on science achievement and interest in science. In line with previous research (e.g., Olitsky, 2007; Stamovlasis et al., 2006; Zady et al., 2003), the results of the study provide empirical support that integrating scientific argumentation into the teaching and learning of science can promote scientific literacy. Therefore, the curriculum developers in Qatar would need to design appropriate instructional strategies to integrate scientific argumentation into the teaching and learning of science. However, integrating aspects of teaching science as inquiry into their planning and instruction is a daunting challenge for teachers (Crawford, 2007). To better support teachers in scientific inquiry, there is a need to develop professional development workshops that more effectively support teachers in engaging in this complex practice (Akerson, Hanson, & Cullen, 2007; Akerson & Hanuscin, 2007; Zohar, 2008). Furthermore, providing opportunities for all students in Qatar to learn how to engage in scientific argumentation in the context of science may foster productive scientific argumentation in science classrooms.

In conclusion, despite poor performance on science assessment, adolescents in Qatar had a high level of interest in science. Because definitive conclusions cannot be drawn on the basis of the results of the present study, the apparent anomalous relationship between science achievement and interest in science warrants further investigation. Adolescents who were struggling with the science proficiency items may have tried to compensate for their low perceived science performance by responding positively on the interest in science items (SCE, 2007). Qatari adolescents’ disappointing performance on science assessment can be attributed to their conspicuous lack of fundamental reading literacy and numeracy skills required for learning the more advanced scientific knowledge and science concepts that were being assessed in the PISA 2006 (OECD, 2007; SCE, 2007). Therefore, it is critical for Qatar to raise the level of literacy in general and science literacy in particular to cope with the demands of global competition and to deal with the challenges of the next century. To this end, the need of the hour is to more carefully and critically examine the context in which students are educated in Qatar. Furthermore, the findings of the present study provide empirical support that science teaching and learning with a focus on models or applications and interactive science teaching and learning can promote adolescents’ scientific literacy and enhance their interest to

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learn science. Engaging students in complex cognitive tasks such as inquiry, argumentation, and explanation would be of significant help in creating citizens who are scientifically literate and capable of understanding scientific reasoning (Corcoran, Mosher, & Rogat, 2009). A change in how science is conducted may invigorate the push for inquiry in the K–12 classroom. Hence the focus of Qatar’s recent K–12 school reform initiative, called Education for a New Era (see Brewer et al., 2007), should be on creating classroom environments that are inquiry-based, and that support their students in developing informed views of scientific inquiry and the nature of science.

Limitations In the present study, there were three characteristics that might have limited the conclusions that could be drawn.

First, the PISA 2006 used student questionnaires to collect information about learning environments. Hence in the present study I relied solely on students’ ratings of instructional practices in science lessons. Although students’ perceptions and ratings are the most appropriate sources of data for assessing the learning environment (L¨udtke et al., 2006, 2009), the reliability of students as data sources has repeatedly been questioned because of the idiosyncratic nature of students’ perceptions of their learning environment (Aleamoni, 1999; L¨udtke et al., 2006, 2009; Marsh & Roche, 1997). To avoid reverse ecological fallacy or atomistic fallacy (Hofstede, 2001), students’ ratings were not aggregated to the school level. More research, such as that being conducted by L¨udtke et al. (2006, 2009), would prove beneficial to address the varied conceptual and methodological challenges associated with the use of students’ ratings of learning environment. Second, the PISA does not take the classroom level into account. As a result, the proportions of between-school variance might have been overestimated in the present study. Moreover, given the large amounts of unexplained variance in the models reported in the study, there is an obvious need to collect data at the classroom level. Finally, conclusions about causal inferences between variables must be made with caution. Large samples may cause statistical significance even though effect sizes are low (Kline, 2009).

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AUTHOR NOTE Shaljan Areepattamannil, PhD, is a research scientist with the Centre for International Comparative Studies, Office of Education Research, National Institute of Education, Nanyang Technological University, Singapore. His research focuses on better understanding the psychological and environmental factors that impact the academic achievement of adolescents.

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APPENDIX HLM Equations Null Models SCIEACH = γ0o + u o j + r i j SCIEINTR = γ0o + u o j + r i j Level 1 Models SCIEACH = γ0o + γ1o (IMMIG)i j + γ2o (HISEI)i j + γ3o (SCAPPLY)i j + γ4o (SCINVEST)i j + γ5o (SCHANDS)i j +γ6o (SCINTACT)i j + u o j + r i j SCIEINTR = γ0o + γ1o (IMMIG)i j + γ2o (SCAPPLY)i j + γ3o (SCINTACT)i j + u o j + r i j Final Models SCIEACH = γ0o + γ01 (SCHHISEI) j + γ1o (IMMIG)i j + γ2o (HISEI)i j + γ3o (SCAPPLY)i j + γ4o (SCINVEST)i j +γ5o (SCHANDS)i j + γ6o (SCINTACT)i j + u o j + r i j SCIEINTR = γ0o + γ01 (SCHHISEI) j + γ1o (IMMIG)i j + γ2o (SCAPPLY)i j + γ3o (SCINTACT)i j + u o j + r i j

TABLE A1. Description of Level 1 and Level 2 Variables Used in the HLM Models Variable name Outcome SCIEACH SCIEINTR Student level GENDER IMMIG HISEI SCAPPLY SCINVEST SCHANDS SCINTACT School level SCHLOCA SCHSIZE SCHHISEI

Description Combined science literacy (science achievement) scale Interest in science scale Gender of student Immigrant status of student Parents’ occupation Science teaching and learning with a focus on model or applications Science teaching and learning using student investigations Science teaching and learning using hands-on activities Interactive science teaching and learning School location School size School average parents’ occupation