Influence of motivation, self-beliefs, and instructional practices on science achievement

Soc Psychol Educ (2011) 14:233–259 DOI 10.1007/s11218-010-9144-9

Influence of motivation, self-beliefs, and instructional practices on science achievement of adolescents in Canada Shaljan Areepattamannil · John G. Freeman · Don A. Klinger

Received: 7 March 2010 / Accepted: 25 October 2010 / Published online: 12 November 2010 © Springer Science+Business Media B.V. 2010

Abstract This study examined the effects of motivation to learn science, science self-beliefs, and science instructional practices on science achievement of 13,985 15-year-old students from 431 schools across Canada. Hierarchical linear modeling (HLM) analyses, while controlling for student- and school-level demographic characteristics, revealed the substantial predictive effects of motivation to learn science, science self-beliefs, and science instructional practices on science achievement of adolescents. Motivational beliefs—self-efficacy and self-concept—and enjoyment of science had substantial positive predictive effects on science achievement. In contrast, general interest in science had a negative predictive effect on science achievement in the context of other variables. Whereas science teaching using hands-on activities had a substantial positive predictive effect on science achievement, science teaching using student investigations had a substantial negative predictive effect in the context of other variables. The final HLM model indicated that only 8% of the variance in science achievement was between schools and 92% of the variance involved students within schools.

Keywords Motivation · Self-beliefs · Instructional practices · Science achievement · Adolescents 1 Introduction Scientific literacy is increasingly important for today’s technological societies (Organization for Economic Cooperation and Development [OECD] 2007). Indeed, science permeates every aspect of modern life and full enculturation into today’s technological

S. Areepattamannil (B) · J. G. Freeman · D. A. Klinger Faculty of Education, Queen’s University, Kingston, ON K7M 5R7, Canada e-mail: shaljan.areepattamannil@queensu.ca

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society necessitates the understanding of science (Rowlands 2008). Further, recent reform efforts in science education underline the importance of developing students’ scientific thinking skills and scientific literacy (e.g., Goodrum et al. 2001; NRC 1996, 2000, 2005, 2007; Millar and Osborne 1998). Nevertheless, the most popular field of study among university students across Canada since 1992 has been social and behavioral sciences since 1992 (Statistics Canada 2009). An alarming decline in student enrollments and interests in science at both high school and university levels has also been reported in Australia (Dekkers and de Laeter 2001; Goodrum et al. 2001), France (Lyons 2006), Germany (Haas 2005), the United Kingdom (Murphy and Beggs 2003), and the United States (Lyons 2006).

Notwithstanding the present decreased popularity of science, Canadian adolescents performed well in science assessments in the third cycle (2006) of the Programme for International Student Assessment (PISA; OECD 2007), with only adolescents from Finland and Hong Kong-China outperforming their Canadian counterparts (OECD 2007). However, there was a large gap in science performance (i.e., equivalent to almost one-half of a proficiency level) between Canadian and Finnish adolescents (see Bussière et al. 2007). Hence there is a need to examine the factors affecting science achievement of adolescents in Canada.

Historically, research on science achievement has focused on cognitive factors such as ability, intelligence quotient (IQ), and other measures of innate aptitude. However, science achievement is also related to other domains, such as affective and motivational characteristics of individuals (e.g., Tuan et al. 2005; House 2006, 2008). Enhancing students’ motivation and their motivational beliefs may not alone promote students’ scientific thinking skills and scientific literacy (Nolen 2003). Instructional contexts that stimulate students’ motivation to inquire and learn and enabling them to use cognitive and metacognitive strategies are also important to enhance students’ scientific inquiry skills and scientific literacy (Yoon 2009). Therefore, the purpose of this study was to examine the influence of motivation to learn science, science self-beliefs, and science instructional practices on science achievement of adolescents in Canada. More specifically, this study addressed the following research question: to what extent do motivation, motivational beliefs (i.e., self-efficacy and self-concept), and instructional practices predict the science achievement of adolescents, after controlling for student and school demographic characteristics?

1.1 Motivation to learn science and science achievement Increasing the motivation of students to learn science—both intrinsic and extrinsic motivation—is at the heart of major reforms in science education (e.g., NRC 1996, 2000). Intrinsic motivation refers to behaviors performed out of interest and enjoyment (Ryan and Deci 2000). In contrast, extrinsic motivation refers to behaviors carried out to attain contingent outcomes (Ryan and Deci 2000). For example, whereas learning science for its own sake is intrinsic motivation to learn science (Eccles et al. 2006), learning science as a means to an end is extrinsic motivation to learn science (Mazlo et al. 2002). Unlike extrinsically motivated students, intrinsically motivated students tend to learn better and are more creative because they willingly devote time and

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energy to their studies (Niemiec and Ryan 2009). Still, more autonomous types of extrinsic motivation—identified and integrated regulation—are also associated with enhanced student learning (e.g., Chirkov and Ryan 2001; Reeve et al. 2002).

Intrinsic motivation to learn science, i.e., interest in and enjoyment of particular science subjects, is important for students’ cognitive engagement, learning, and achievement (e.g., Ainley et al. 2005, 2002; Hidi et al. 2006; Osborne et al. 2003).

Students with an interest in science topics and who enjoy learning science tend to be emotionally attached to learning science and perceive learning science as a meaningful activity (e.g., Glaser-Zikuda and Fusz 2008; Glaser-Zikuda et al. 2005). In other words, the presence of high interest reduces students’ need to consciously and effortfully persist in an activity because they use their repertoire of self-regulated learning strategies to guide and enhance their learning process (see Hidi and Renninger 2006; Renninger and Hidi 2002). In short, “the combination of interest and self-regulation has the potential to facilitate learning of the broad range of skills and competencies students need for productive and creative futures” (Hidi and Ainley 2008, p. 101).

Likewise, extrinsic motivation to learn science—instrumental and future-oriented motivation—is an important predictor of course selection, career choice, and achievement (e.g., Hassan 2008; House 2009; Lavigne and Vallerand in press). Students are extrinsically motivated to learn science when they perceive science to be useful to them for either their future studies or careers (OECD 2007). Perceived instrumentality may function as a helpful incentive when a student has to do school work that is not inherently pleasurable (see Greene et al. 2004; Miller and Brickman 2004; Walker and Greene 2009). Thus perceived instrumentality enhances not only student motivation but also their subsequent performance (Eccles and Wigfield 1995; Miller and Brickman 2004; Miller et al. 1996; Simons et al. 2004; Vansteenkiste et al. 2004; Wigfield and Eccles 2002).

Students’ self-beliefs, a critical component of motivation, also influence their academic motivation, their self-regulated learning strategies, and the academic success they ultimately attain (Bandura 1986; Pajares 2008; Zeldin and Pajares 2000). Hence it is crucial to examine the influence of academic self-beliefs on students’ science achievement.

1.2 Science self-beliefs and science achievement Science self-beliefs—self-efficacy and self-concept—and science achievement are key components of scientific literacy (Wang et al. 2008). Self-efficacy refers to students’ self-perceived confidence to succeed in specific scientific tasks, science courses, or science-related activities (see Britner and Pajares 2001, 2006; Lawson et al. 2007).

On the other hand, self-concept refers to students’ self-perceived academic ability (Bong and Skaalvik 2003). For example, a student’s expectation to achieve an A on her next science test is a self-efficacy judgment, whereas the statement “I have always done well in science classes” is a self-concept judgment (e.g., Marsh 1990). While self-efficacy is more future-oriented and malleable, self-concept is more past-oriented and stable (see Bong and Clark 1999; Bong and Skaalvik 2003).

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Students with positive self-efficacy beliefs in science are more likely to use effective cognitive and regulatory strategies in a systematic way (see Neber and SchommerAikins 2002; Pintrich and DeGroot 1990). Hence self-efficacy beliefs in science influence students’ science achievement (Britner 2008; Britner and Pajares 2001, 2006; Caprara et al. 2008; Ee et al. 2003; Hidi et al. 2006; House 2008; Lavonen and Laaksonen 2009; Yoon 2009; Zusho and Pintrich 2003), their choices of sciencerelated activities (Kupermintz 2002; Little and Rubin 2002; Lodewyk and Winne 2005), the effort they expend on those activities (Pajares 2008; Schunk and Ertmer 2000; Walker and Greene 2009), the perseverance they show when encountering difficulties (Dweck and Master 2008; Pajares 2008), and the ultimate success they experience in science (Britner and Pajares 2001, 2006; House 2008; Zeldin et al. 2008; Zeldin and Pajares 2000).

Similarly, students with strong and positive self-concept perceptions set challenging academic goals for themselves (e.g., Marsh et al. 2005), persist longer on difficult tasks (e.g., Dermitzaki et al. 2009; Marsh et al. 2008; Trautwein et al. 2006), take advanced course work (e.g., Marsh 1993; Marsh and Yeung 1998), feel less anxious in achievement situations (e.g., Skaalvik and Rankin 1996; Zeidner and Schleyer 1999), do not drop out of school (e.g., House 1993), enjoy their academic work more (e.g., Marsh and Yeung 1997), and have higher levels of long-term educational attainment (e.g., Marsh and O’ Mara 2008). Indeed, self-concept is a determinant of academic achievement (Guay et al. 2003; Marsh and Craven 2006; Marsh and O’ Mara 2008; Möller et al. 2009; Valentine and DuBois 2005; Valentine et al. 2004). Students with high science self-concept tend to perform well in science assessments (e.g., Britner 2008; Chien et al. 2008; Chiu 2008; Ireson and Hallam 2009; Lavonen and Laaksonen 2009).

Undoubtedly, motivation to learn science and motivational beliefs positively influence students’ science achievement. However, classroom instructional characteristics have been found to be more influential than motivational factors with respect to students’ science achievement and their satisfaction with learning science (see Nolen 2003). Therefore, it is also critical to examine the influence of instructional practices on science achievement of adolescents.

1.3 Science instructional practices and science achievement Science reform initiatives in the last three decades have shifted the focus of science education from teacher-driven instruction to student-centered instruction (e.g., NRC 1996, 2005). Whereas the traditional teacher- driven instructional strategies to teach science focus on the didactic presentation of a static body of facts isolated within disciplinary boundaries (Crawford 2005; Polman and Pea 2001; Southerland et al. 2003), studentcentered instructional strategies to teach science emphasize critical thinking and problem-solving skills (Lehrer and Schauble 2006; McNeill and Pimentel 2009; Statistics Canada 2009). In student-centered instruction, learning science is active and socially constructive (Beamer et al. 2008; Bentley et al. 2007; Lemke 1990, 2001; Ozkal et al. 2009; Palmer 2008; Wu and Tsai 2005), involving scientific inquiry (Blanchard et al. 2009; Duschl et al. 2007; Kim et al. 2007), dialogic interactions (Linn et al. 2003; McNeill and Pimentel 2009; Statistics Canada 2009; Scott et al. 2006; Yerrick and

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Roth 2005), and hands-on activities (Hofstein et al. 2008; Hofstein and Lunetta 2004; Kipnis and Hofstein 2008; Lunetta et al. 2007).

Current reform efforts in science education strongly advocate the use of scientific inquiry or inquiry-based science strategies in K-12 classrooms (Bybee 2000; Hofstein and Lunetta 2004; NRC 2000, 2001; Sere 2002). Teaching science as inquiry entails engaging students in asking scientific questions (NRC 2000, 2007; Windschitl 2003), conducting investigations to answer those questions (Howes et al. 2009), and building evidence-based explanations (Crawford 2000, 2007; Krajcik et al. 2000).

Inquiry-based science “assists in constructing and understanding of scientific concepts, learning how to learn, becoming an independent and lifelong learner, and further developing the habits of mind associated with science” (Martin et al. 2009, p. 130).

Without doubt, inquiry-based science teaching and learning would promote students’ science achievement (see Geier et al. 2008; Lawrenz et al. 2009; Wolf and Fraser 2008).

Dialogic classroom discourse is an indispensable element of constructivistinformed science classroom teaching (Berland and Reiser 2009; McNeill and Pimentel 2009; Palmer 2008; Statistics Canada 2009; Scott et al. 2006). The traditional pattern of discussion in science classrooms—authoritative classroom discourse—places teachers in a position of power in which they control the topic, the direction of the conversation, who participates in the conversation, and what contributions count as legitimate (Lemke 1990, as cited in McNeill and Pimentel 2009). Hence authoritative classroom interactions thwart the social construction of knowledge in science classrooms by drastically minimizing student-to-student interaction (McNeill 2009).

In contrast, in dialogic interactions, “the teacher encourages students to put forward their ideas, explore and debate points of view, and students’ responses are often tentative suggestions based on open or genuine questions, spontaneous, and expressed in whole phrases or sentences” (Chin 2007, p. 816). Science instruction that incorporates dialogic classroom interactions leads to higher student achievement and student engagement in science (e.g., Driver et al. 2000; Duschl and Osborne 2002; Freedman 1997; Stamovlasis et al. 2006; Stohr-Hunt 1996).

Hands-on activities—part and parcel of scientific inquiry—also play a distinctive and pivotal role in science teaching and learning (Hofstein et al. 2008). Mounting evidence suggests that hands-on science activities have the potential to enhance students’ higher-order learning skills such as metacognition and argumentation (e.g., Dori and Sasson 2008; Dori et al. 2004; Hofstein 2004; Hofstein and Lunetta 2004; Kaberman and Dori 2009a,b; Kipnis and Hofstein 2008; Lunetta et al. 2007). Moreover, students exposed to hands-on science instruction frequently get significantly higher scores in science than those students who experienced hands-on science infrequently (see Echevarria 2003; Freedman 1997; Klahr et al. 2007; McCarthy 2005; Stohr-Hunt 1996).

In sum, in addition to motivation and motivational beliefs, classroom instructional practices are important for understanding and improving educational processes.

They shape students’ learning environment and influence student motivation, selfbeliefs, and achievement. Whereas controlling educational climates undermine students’ intrinsic motivation, autonomy supportive teaching practices increase students’ intrinsic motivation and perceived competence (e.g., Black and Deci 2000; Burton et al.

2006; Chirkov and Ryan 2001; Reeve et al. 2002; Tsai et al. 2008). Although students

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can enhance their learning by setting intrinsic goals rather than extrinsic goals (Lens and Vansteenkiste 2008), students also learn better when they have well-internalized extrinsic motivation for learning (Niemiec and Ryan 2009). All in all, it is important to investigate the influence of motivation to learn science, science self-beliefs, and instructional practices on science achievement of adolescents.

2 Method 2.1 Sample Data for the present study were drawn from the Canadian sample of the PISA 2006 that contained 22,646 students aged 15 years who were selected through stratified random sampling procedures from 896 schools across the 10 provinces of Canada.

This sample consisted of 2,608 immigrant children (1,314 male, 1,294 female) and 19,135 non-immigrant children (9,283 male, 9,852 female). Another 903 (4%) students did not have information on their immigration status. There is no consensus in the research literature with respect to determination of sample sizes at each level of a cluster randomized trial (see Bickel 2007; Heck and Thomas 2000; Hox 2002; Maas and Hox 2005; Mok 1995; Scherbaum and Ferreter 2009; Spybrook 2008). The number of students per school in the Canadian sample varied between 1 and 225.

Since less bias and efficiency can be expected from sample designs involving more clusters and fewer observations per cluster than sample designs involving fewer clusters and more observations per cluster (see Mok 1995), we chose schools with at least 20 students, resulting in a final sample of 13,985 (6,799 male, 7,186 female; 12,377 non-immigrant, 1,608 immigrant) students nested within 431 schools (273 rural, 158 urban; mean school size = 32). We used the Optimal Design software (OD Version 2.0; Liu et al. 2009) for statistical power and sample size computations, and we found adequate statistical power for the current study (power > 0.80).

2.1.1 Handling missing data To include the maximum number of students and schools in the analyses, missing values for student- and school-level items were imputed prior to forming scales. To avoid the underestimation of standard errors, we employed multiple imputation (MI; Little and Rubin 2002; Rubin 1987; Schafer 1997) to fill in the missing data (Allison 2002; Newman 2003; Peugh and Enders 2004). Given that more than 5% of the student- and school-level cases had one or more missing values, we created 10 independent imputed data sets for each level using PASW Missing Values’ multiple imputation procedure (SPSS 2009) to obtain 96% efficiency (Rubin 1987). We conducted statistical analyses separately for each imputed data set. The parameter estimates across the analyses were averaged and standard errors were combined, taking into account both the variance in the parameter within each analysis and the variability between imputed data sets (see Schafer and Olsen 1998).

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2.2 Measures 2.2.1 Outcome measure Scientific literacy (science achievement) is the dependent variable of interest for analyses using data from PISA 2006 as it was identified as the primary domain for this assessment cycle. PISA approaches scientific literacy as the application of science knowledge and skills (OECD 2007). Scientific literacy is defined as: An individual’s scientific knowledge and use of that knowledge to identify questions, acquire new knowledge, explain scientific phenomena, and draw evidence-based conclusions about science-related issues; understanding of the characteristic features of science as a form of human knowledge and enquiry; awareness of how science and technology shape our material, intellectual, and cultural environments; and willingness to engage in science-related issues and with the ideas of science, as a reflective citizen. (OECD 2006, p. 12) In the 2006 science literacy assessment, PISA provided scores on a combined science 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 2009a). We used the plausible values feature of HLM 6.08 for Windows (Raudenbush et al. 2004), prompting the program to run models for each of the five PVs internally, producing their average value and correct standard errors.

2.2.2 Student-level measures The student-level index variables reported in this study are in their original PISA measurement scales. Adolescents’ motivation to learn science was measured using four measures—enjoyment of science, general interest in science, instrumental motivation to learn science, and future-oriented motivation to learn science (see Table 1).

Enjoyment of science (5 items, α = .94) measured adolescents’ enjoyment in learning and reading about broad science, solving broad science problems, and acquiring new knowledge in broad science (e.g., “I generally have fun when I am learning broad science topics”). Items were rated on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). General interest in science (8 items, α = .84) measured adolescents’ interest in learning about broad science topics (e.g., “topics in physics”).

Items were rated on a 4-point Likert scale ranging from 1 (no interest) to 4 (high interest).

Instrumental motivation to learn science (5 items, α = .94) measured adolescents’ beliefs that science would be useful for future employment or education (e.g., “I study school science because I know it is useful for me”). Future-oriented motivation to learn science (4 items, α = .93) measured adolescents’ beliefs that they would study and work in the field of science as an adult (e.g., “I would like to spend my life doing advanced broad science”). Items on both measures were rated on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree).

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Table 1 Descriptive and distributional statistics, and Cronbach’s alphas Student-level variable

M

SD

Skew Kurtosis Cronbach’s α

−0.04 −1.99

Student demographics Gender

0.51

0.50

Immigrant status

0.21

0.32

3.52

–

Occupational status self

60.35

17.96 −0.22 −0.76

–

Highest parental occupational status

52.88

15.71

No. of books in the home

3.63

1.37

2.35

–

0.03 −0.56

–

−0.02 −0.68

–

Motivation and self-beliefs Enjoyment of science

2.73

0.74

−0.37 −0.12

.94

General interest in science

2.55

0.64

−0.41 −0.08

.84

Instrumental motivation to learn science

2.96

0.78

−0.50 −0.17

.94

Future-oriented motivation to learn science

2.31

0.88

0.20 −0.80

.93

Self-efficacy in science

2.84

0.61

−0.41

0.08

.86

Self-concept in science

2.72

0.73

−0.29 −0.14

.94

M

SD

Skew Kurtosis ICC[2]

School location

0.37

.48

School size

783.62 468.20

School-level variable School demographics

0.55 −1.69

–

0.98

–

1.56

Instructional practices −0.01 −0.30

Science teaching with a focus on models or applications 2.67

0.20

Science teaching using student investigations

1.83

0.24

Science teaching using hands-on activities

2.44

0.23

−0.28

0.29

.67

Interactive science teaching

2.60

0.25

−0.34

0.18

.76

0.13

0.32

.78 .77

All student- and school-level scales ranged from 1 to 4

Adolescents’ science self-beliefs were measured using two measures—self-efficacy and self-concept in science. While self-efficacy in science (8 items, α = .86) measured adolescents’ confidence to perform science-related tasks (e.g., “predict how changes to an environment will affect the survival of certain species”), self-concept in science (6 items, α = .94) measured adolescents’ perceptions of their ability to learn science (e.g., “I learn school science topics quickly”). The self-efficacy items were rated on a 4-point Likert scale ranging from 1 (I couldn’t do this) to 4 (I could do this easily); and the self-concept items were rated on 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree).

2.2.3 School-level measures Since the PISA sample is not class based, PISA 2006 did not collect data at the classroom/teacher level (see OECD 2009b). In educational research, however, students are generally asked to evaluate features of their lessons to assess the characteristics of the learning environment (Lüdtke et al. 2006). In the PISA 2006 science assessment, 17

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items asked students how often specific teaching activities occurred in regular science lessons. The PISA 2006 used 15 of these items to construct four individual-level scales.

To estimate the influence of instructional practices on science achievement, we aggregated these 15 student-level items to the school-level and created aggregate scales for instructional practices. The aggregated scores reflect perceptions of the shared learning environment, corrected for individual idiosyncrasies (Kunter et al. 2007; Lüdtke et al.

2006). “The aggregate items, create aggregate scales (CAS) approach is appropriate when theoretically aggregate phenomena are being studied and there are few items for each individual-level indicator” (Peterson and Castro 2006, p. 514).

Prior to aggregating scores and forming scales, we assessed the psychometric properties of the items and the aggregatability of the scales (r W G(4) > .70; see LeBreton and Senter 2008). Since the individual-level reliability estimates, such as Cronbach’s alpha, are not appropriate for assessing the reliability of aggregate-level variables (see Jeon et al. 2008; LeBreton and Senter 2008; Lüdtke et al. 2006), we used the intraclass correlations ICC(1) and ICC(2) to determine whether or not aggregated student-level ratings were reliable indicators of school-level constructs (Bliese 2000; Raudenbush and Bryk 2002). The ICC(1) and ICC(2) indices are based on a one-way analysis of variance (ANOVA) with random effects. The ICC(2), which is computed on the basis of the ICC(1) and the mean school size, indicated that the reliability of the individual student ratings aggregated to the school-level was satisfactory. Only one scale (science teaching using hands-on activities) was below the critical value of 0.70.

However, the overall impression was that the aggregated student ratings of instructional practices distinguished reliably among schools. All school-level items were measured on a 4-point Likert scale ranging from 1 (never or hardly ever) to 4 (in all lessons).

Science teaching with a focus on model or applications (4 items; ICC[2] = .78) 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”). Science teaching using student investigations (3 items; ICC[2] = .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”).

Science teaching using hands-on activities (4 items; ICC[2] = .67) 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”). Interactive science teaching (4 items; ICC[2] = .76) measured the frequency with which interactive teaching activities occurred in regular science lessons (e.g., “the lessons involve students’ opinions about the topics”).

2.2.4 Control variables We used control measures to control for potential specification errors in estimating the effects of student- and school-level factors on adolescents’ science achievement.

Gender, immigrant status, socioeconomic status (SES), school location, and school enrollment size have been shown to be important predictors of academic achievement in prior research (e.g., Bussière et al. 2007; Caro et al. 2009; Fuligni and Fuligni 2007;

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Klinger et al. 2006; Laurie 2009; McBride et al. 2009). Therefore, we used gender, immigrant status, and SES indicators as control variables at the student-level; and school location and school enrollment size as control variables at the school-level.

Gender (male = 0, female = 1), immigrant status (non-immigrant = 0, immigrant = 1) and school location (rural = 0, urban = 1) were coded as dichotomous variables. School enrollment size—the total enrollment of students in a school—was used as a continuous variable.

To create an SES measure, we conducted exploratory factor analyses using maximum likelihood with oblique rotation to examine the factor structure and the psychometric properties of items included in the PISA 2006 index of economic, social, and cultural status (ESCS). Moreover, we examined bivariate correlations between the items included in ESCS and the outcome measure. Most of the items included in the ESCS index did not have adequate factor loadings, creating a scale that lacked sufficient internal consistency, and were negatively and poorly correlated with the outcome measure. The three items that had the highest positive correlation with the outcome measure were the occupational status of self, the highest occupational status of parents, and the number of books in the home. Therefore, in line with previous research (e.g., Braun et al. 2006; Martin et al. 2008), we used these three single explanatory variables as measures of student SES in the present study. They were included as continuous variables in the analyses.

2.3 Statistical analyses 2.3.1 Hierarchical linear modeling (HLM) Much of social science data, such as the student achievement outcome in the present study, is usually nested in a multilevel hierarchy. For instance, a student’s test score not only explains abilities and characteristics inherent within that student, but the score may also be affected by factors within the student’s classroom, school, and community. In recent years, there has been a gradual transition among educational researchers from employing multiple regression techniques to exploring multiple levels of influence on educational outcomes with hierarchical linear modeling (HLM) (e.g., Luyten et al. 2008; Klinger et al. 2006; Ma and Crocker 2007; Trautwein et al. 2009). HLM is widely acknowledged as the statistical technique most appropriate for analyzing data that describe hierarchical organizations, such as educational systems (Raudenbush and Bryk 2002). The strength of hierarchical analysis is that it estimates statistics for each unit of a hierarchical structure using data from that unit while borrowing strength from the information available on all units (Willms 1999).

We used two-level HLM (Raudenbush and Bryk 2002) to analyze the data, with students at Level 1 and schools at Level 2. The random-intercepts model with fixed slopes (Raudenbush and Bryk 2002) was employed. Dichotomous variables were retained in their original metric. All continuous student- and school-level variables were centered on the grand mean. Centering makes the interpretation of multilevel results more meaningful (Raudenbush and Bryk 2002) and “removes high correlations between random intercepts and slopes, and high correlations between first- and second-level

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variables and cross-level interactions� (Kreft and De Leeuw 1998, p. 114). Grandmean centering essentially produces a model mathematically equivalent to the raw score model without any centering (Enders and Tofighi 2007). Moreover, normalized student- and school-level weights were employed in the HLM analyses (see OECD 2009b). For all analyses, the solutions were generated on the basis of full maximumlikelihood estimation (FIML). Restricted maximum likelihood (REML) estimates of variance-covariance components adjust for the uncertainty about fixed effects; FIML estimates do not (Raudenbush and Bryk 2002).

Before conducting the HLM analyses, a systematic sequence of exploratory unweighted regression models were first fitted using the General Linear Models (GLM) methodology. Variables that emerged from the GLM analyses were entered into the regression by category, according to a predetermined order, based on both statistical considerations and interpretive goals. At the first stage, we ran a null model containing only the outcome variable and no independent variables to obtain a decomposition of score variance into within-school and between-school components. This simple multilevel model is statistically equivalent to a one-way random effects ANOVA (Raudenbush and Bryk 2002). At the succeeding stages, a set of variables corresponding to a particular characteristic (i.e., student demographic characteristics, motivational characteristics, school demographic characteristics, instructional characteristics) were entered into the regression stepwise. At each stage, we retained the set of variables corresponding to a particular characteristic only if the regression coefficients associated with the variables in the set exceeded a predefined statistical threshold (i.e., p < .05) and had a regression coefficient of at least 0.05. Thus the variables that remained from the previous stage together with all the variables in the next category were entered into the regression. We continued this process until the last category was entered into the regression. Hence the final regression model in this study was the culmination of a sequence of exploratory analyses that systematically and sequentially examined the relationships between science achievement scores and four categories of student- and school-level variables. All HLM analyses were first conducted with the 10 complete data sets, and the integrated results are reported (see Raudenbush and Bryk 2002).

3 Results 3.1 Descriptive statistics and zero-order correlations Descriptive statistics and zero-order correlations for the Level 1 constructs and for the aggregated Level 2 constructs are presented in Tables 1 and 2. Most of the student- and school-level factors were positively and significantly correlated to a moderate degree (Tabachnick and Fidell 2007). At the student-level, the highest positive correlation was between instrumental motivation to learn science and future-oriented motivation to learn science (r = .71, p < .001). At the school-level, the highest positive correlation was between science teaching with a focus on model or applications and interactive science teaching (r = .62, p < .001).

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Table 2 Zero-order correlations for the student- and school-level scales Scale

Correlation 1

2

3

4

5

6

Student-level 1. Enjoyment of science

–

2. General interest in science

.69***

–

3. Instrumental motivation to learn science

.60***

.56***

4. Future-oriented motivation to learn science

.66***

.59***

.71***

–

5. Self-efficacy in science

.48***

.45***

.34***

.36***

–

6. Self-concept in science

.63***

.52***

.55***

.56***

.51***

–

–

School-level 1. Science teaching with a focus on models

–

or applications 2. Science teaching using student investigations

.44***

3. Science teaching using hands-on activities

.37***

– .31***

–

4. Interactive science teaching

.62***

.57***

.15**

–

** p < .01 ∗ ∗ ∗ p < .001

3.2 Predicting science achievement The purpose of the study was to examine the effects of motivation, self-beliefs, and instructional practices on science achievement of adolescents. To address the purpose of the study, five hierarchical linear models were fitted (see Table 3). Model 1, the traditional HLM null model, indicated that the average science achievement (γˆ00 = 541.5) varied across schools in Canada, χ 2 (430, N = 13985) = 2795.90, p < .001. The overall estimate of reliability was found to be λˆ = .84, indicating that the sample means tended to be a reliable indicator of true school means. It also revealed that 14% of the variance in science achievement was between schools (ρˆ = .14) and 86% of the variance involved students within schools.

Model 2 included the effect of student demographic controls (γˆ00 = 547.4).

It revealed that all student-level demographic variables were statistically significant predictors of science achievement. There was a substantial negative predictive effect of immigrant status (–21.42) on science achievement in the context of other variables.1 The number of books in the home had the largest positive predictive effect on science achievement (+12.68). Although statistically highly significant and positive, in comparison to other predictors in the model, the predictive effects of student occupational status and highest parental occupational status were small. The overall measure of reliability for Model 2 was λˆ = .80. The addition of the student demographic vari-

1 For the sake of brevity, “in the context of other variables” is not repeated, but should be assumed by the

reader.

123

541.55 (2.80)

Fixed effects Intercept (γ00 )

20.01* (2.35)

Self-efficacy in science

Self-concept in science

– 9.01 (14.22) –72.45* (8.81) 33.59*(8.71) 13.90(10.55)

Science teaching using hands-on activities

Interactive science teaching

–

Science teaching using student investigations

0.01* (0.00)

20.33* (1.97)

42.58* (3.29)

–

–

–12.21* (2.18)

17.94* (2.29)

6.97* (1.15)

0.48* (0.06)

0.59* (0.08)

–20.48* (3.95)

–

539.82 (1.69)

Model 5

Science teaching with a focus on models or applications

5.24 (4.28)

School size

19.47* (2.18)

42.70* (3.34)

3.79 (2.09)

–

–13.53* (2.30)

16.36* (2.52)

7.27* (1.09)

0.48* (0.06)

0.56* (0.08)

–20.00* (3.70)

–

539.99 (2.14)

Model 4

School location

School-level

4.73* (2.04) 42.72* (3.36)

Future-oriented motivation to learn science

–2.27 (2.71)

7.17* (1.09) –13.25* (2.39)

12.68* (1.22)

No. of books in the home

0.50* (0.06)

0.56* (0.08)

Instrumental motivation to learn science

0.73* (0.09)

Highest parental occupational status

General interest in science

1.17* (0.09)

Occupational status self

–19.95* (3.81)

1.65 (2.64)

16.59* (2.64)

–21.42* (4.79)

540.21 (2.45)

Model 3

Enjoyment of science

–9.08* (2.67)

Immigrant status

547.41 (2.64)

Model 2

Gender

Student-level

Model 1

Parameter

Table 3 Fixed effects estimates and variance–covariance estimates for models of the predictors of science achievement

Influence of motivation, self-beliefs, and instructional practices 245

123

123 36 13

NA

.11

.14 NA

761.32 6350.85

1203.93

Model 2

7337.11

Model 1

Number of students = 13, 985. Number of schools = 431. Standard errors of the estimates are in parentheses * p < .05

Variance in achievement between schools explained (%) Variance in achievement within schools explained (%)

Intercept variance (τˆ00 )

Level 1 variance σˆ 2

Intraclass correlation ρˆ

Random effects

Parameter

Table 3 continued

30

43

.12

5070.38

676.38

Model 3

30

45

.11

5070.84

655.95

Model 4

30

61

.08

5078.02

467.11

Model 5

246 S. Areepattamannil et al.

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ables explained 36% of the between-school variance and 13% of the within-school variance in Model 2.

Model 3, which added motivation and motivational beliefs variables as predictors of science achievement (γˆ00 = 540.2), revealed that all student-level predictors were statistically significant, except gender and instrumental motivation to learn science.

While science self-efficacy, science self-concept, enjoyment of science, and number of books in the home had substantial positive predictive effects on science achievement; immigrant status and general interest in science had substantial negative predictive effects on science achievement. Science self-efficacy had the largest predictive effect on science achievement (+42.72). Model 3 explained an additional 7% of the variance between schools (now 43% instead of 36% in Model 2) and an additional 17% of the variance within schools (now 30% instead of 13% in Model 2). This model yielded a reliability of λˆ = .81.

Model 4 added school demographic controls (γˆ00 = 539.9). The model showed that once school location and school size were added, the results were more or less similar to those of Model 3. Compared to the influence of student demographic controls on science achievement, the predictive effects of school demographic controls on science achievement were trivial. Statistically significant predictors in Model 3 retained their statistical significance in Model 4, except future-oriented motivation to learn science.

The inclusion of school-level demographics in Model 4 explained an additional 2% of the variance between schools. The reliability of the model was λˆ = .81.

In the final model (Model 5), we added the four school-level instructional practices variables (γˆ00 = 539.8). After controlling for student demographics, the coefficients for the various student-level variables were not markedly different from those in Model 4. Among the four instructional practices variables, two variables had substantial effects on science achievement. Whereas science teaching using hands-on activities had a substantial positive predictive effect on science achievement (+33.59), science teaching using student investigations had a substantial negative predictive effect on science achievement (–72.45). The percentage of variance between schools (intraclass correlation coefficient) was reduced from 14% in the null model to 8% in the final model. The final model explained 61% of the variance between schools and 30% of the variance within schools. The reliability of the model was λˆ = .75.

4 Discussion The purpose of the study was to investigate the predictive effects of affective variables such as motivation to learn science and motivational beliefs, and science instructional practices on science achievement of adolescents in Canada. The results of the study indicated that the majority of the variance explained was due to student-level factors.

The effects of student-level demographic characteristics on science achievement were somewhat consistent from Models 2–5. In contrast, the school-level demographic controls had no significant meaningful impact on science achievement. Consistent with previous research (e.g., Braun et al. 2006; Martin et al. 2008), one of the SES indicators in the study—number of books in the home—had a positive effect on science achievement, indicating that adolescents with a greater number of books in the

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home had higher science achievement. Similarly, albeit small regression weights in the context of other variables, student occupational status and highest parental occupational status were positively associated with science achievement. Thus, congruent with previous research (e.g., Bussière et al. 2007; Caro et al. 2009), adolescents from higher socioeconomic backgrounds tended to have higher science achievement than those from lower socioeconomic backgrounds. In other words, Canada is leaving some children behind in science, and they tend to be the less privileged.

4.1 Motivation to learn science, science self-beliefs, and science achievement While intrinsic motivation to learn science—interest in and enjoyment of particular science subjects—had a considerable effect on science achievement of adolescents, extrinsic motivation to learn science—instrumental and future-oriented motivation— had no effect on science achievement. In line with previous research (e.g., Ainley et al.

2005; Hidi et al. 2006), there was a positive relationship between enjoyment of science and science achievement, suggesting that adolescents who reported higher levels of enjoyment of science had higher science achievement than those who reported lower levels of enjoyment of science. In contrast, interest in science had a negative effect on science achievement of adolescents. This finding may appear surprising, but the inverse relationship between interest in science and science achievement is in congruence with the findings of other international comparative studies (e.g., Shen and Tam 2008).

Motivational beliefs—self-efficacy and self-concept—also had a significant effect on science achievement of adolescents. Compared to other student-level predictors, self-efficacy and self-concept in science had a very strong and positive relationship with science achievement. Since motivational beliefs mediate the effects of prior achievement, knowledge, and skills on subsequent achievement (Schunk 1985), these beliefs are often better predictors of academic achievement (Bandura 1986). Consistent with previous research (e.g., Britner 2008; Caprara et al. 2008; House 2008; Lavonen and Laaksonen 2009), adolescents with higher levels of confidence in performing science-related tasks and with a more positive perception of their ability to learn science tended to have higher achievement in science. The strong influence of self-efficacy and self-concept on science achievement provides opportunities for teachers and parents to support students’ developing motivational beliefs.

4.2 Science instructional practices and science achievement The findings of the study also indicated an association between instructional practices and science achievement. The hands-on learning approach, which enables students to learn from experiments conducted either by the individual student or by the teacher, had a substantial positive effect on science achievement. Adolescents who reported that their teachers employed hands-on activities in science lessons tended to have higher achievement in science than their peers who reported that their teachers did not employ hands-on activities. This finding is consistent with previous research (e.g.,

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Echevarria 2003; Freedman 1997; Klahr et al. 2007; McCarthy 2005). Hands-on activities have the “potential to enhance students’ conceptual and procedural understanding, their practical and intellectual skills, and their understanding of the nature of science” (Hofstein et al. 2008, p. 59). Hence teachers can play a crucial role in helping students have productive experiences to promote the desired learning in science and their subsequent performance in science.

Science teaching with a focus on model or applications and interactive science teaching had no effect on science achievement of adolescents. However, science teaching using student investigations had a significant negative effect on science achievement. Given the voluminous literature pertaining to the positive effects of inquiry-based science on science achievement (e.g., Geier et al. 2008; Lawrenz et al.

2009; Wolf and Fraser 2008), this finding is counter-intuitive. However, inquiry-based science teaching and learning was also found to be a strong negative predictor of science performance among Finnish adolescents (see Lavonen and Laaksonen 2009).

Moreover, Tretter and Jones (2003) reported that the use of an inquiry-based teaching style did not dramatically alter students’ overall achievement. The use of a student inquiry-based teaching style does not seem to be an effective instructional technique if the goal is limited to increasing proficiency achievement as measured by PISA. The findings of the study warrant further research to investigate the nuanced interplay between inquiry-based science and science achievement.

In conclusion, compared to school-level factors, student-level factors were the best predictors of science achievement of adolescents in Canada. Clearly, intrinsic motivation (i.e., enjoyment of science) in general and motivational beliefs in particular played a significant role in these adolescents’ learning in science classrooms. Therefore, enhancing students’ intrinsic motivation and their motivational beliefs may not only promote students’ scientific thinking skills but also their scientific literacy. Reeve et al. (2002) postulates that “while the effort to learn how to integrate students’ motivational resources into the school curriculum requires asking teachers to develop new skills and brave the waters of conceptual change, the benefits for students of doing so are many” (p. 199).

4.3 Limitations of the study There are two limitations of the study. First, since HLM does not provide tests of the appropriateness of aggregation or nonaggregation (Castro 2002), we aggregated the student-level variables pertaining to classroom instructional practices to the school-level using less rigorous yet still acceptable aggregation criteria (i.e., based on r W G(J ) and ICC[2] indices). Although the aggregated scores reflect perceptions of the shared learning environment, generalizing a relationship that was found on an individual-level to the aggregate-level may pose methodological challenges. Such generalizations may result in reverse ecological fallacy or atomistic fallacy (Hofstede 2001) or what Bliese (2000) refers to as the “fuzzy composition process” (p. 369).

In this situation, the aggregate variable represents a similar yet different construct than its lower-level counterpart; “the aggregate maintains close links to its lower-level

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counterpart but nevertheless differs in subtle and important ways” (Bliese 2000, p.

369).

Second, since the PISA 2006 science assessment did not take the classroom or teacher- level into account, the proportions of between-school variance might be overestimated in PISA 2006. As a result, the variance associated with the classroom or teacher-level was attributed to the neighboring level, i.e., to the school-level. The negative relationship between science teaching using student investigations and science achievement, and the non-significant relationship between other elements of inquirybased science teaching (i.e., science teaching with a focus on model or applications and interactive science teaching) and science achievement suggest the need for further investigation with better, more direct measures of correlates of learning outcomes and the development of appropriate instrumentation to collect this information. In short, in light of the findings of the present study, there is an obvious need to collect data at the classroom or teacher-level because the possibilities for drawing conclusions about the relative role of different levels in PISA 2006 data on students’ science achievement were limited.

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Author Biographies Shaljan Areepattamannil is a Ph.D. candidate in educational psychology at the Faculty of Education, Queen’s University, Canada. His research focuses on the influence of psychological and environmental factors on the academic achievement of adolescents.

John G. Freeman is an associate professor at the Faculty of Education, Queen’s University, with a crossappointment to the School of Health Sciences and Kinesiology and Director of the Social Program Evaluation Group (SPEG). His research and that of the students he supervises focuses primarily on how schools can be made more welcoming places for students, teachers, and parents, regardless of ability level, race, sexuality, or emotional health (among other considerations). As Director of SPEG, he hopes to build rich research partnerships between local community partners and Queen’s University.

Don A. Klinger is an associate professor in assessment and evaluation at the Faculty of Education at Queen’s University, Canada. His research explores the use of large-scale assessments and databases to inform educational policy and practice, and help us determine those factors associated with educational outcomes.

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