Academic Motivation and Engagement: A Domain Specific Approach

Transcription

1 GRE05384 Academic Motivation and Engagement: A Domain Specific Approach Jasmine Green, Andrew J. Martin and Herbert W. Marsh, SELF Research Centre, University of Western Sydney, Australia This research reports on findings from a large-scale study amongst Australian high school students investigating the need to distinguish between academic motivation for mathematics, English and science subjects (with an additional focus on gender and year-level differences). This paper utilises the Student Motivation and Engagement Wheel (Martin, 2003) as a basis for conceptualising academic motivation and engagement and the Student Motivation and Engagement Scale (SMES Martin, 2001, 2002) as a basis for measuring it. A total of 1,801 students from six government high schools were administered the Student Motivation and Engagement Scale. Confirmatory factor analysis (CFA) provided support for the domain specificity of motivation and engagement across three distinct high school subject areas. Additional analyses utilising multiple-indicator-multiple-cause (MIMIC) modelling identified a general trend that girls are more motivated across subjects than boys. The few significant interactions broadly showed that as girls move into middle high school they are more motivated than boys in mathematics and less motivated in science and English. Implications for pedagogy and further research are discussed. The conceptualisation of academic motivation and engagement in the psychological literature encompasses a diverse array of theoretical viewpoints. Thus far, motivation research has shown a tendency to adopt single theories of motivation and engagement in order to understand student behaviour in the classroom. Accordingly studies that successfully tie various theoretical perspectives together into a coherent framework are relatively few. Despite this, various researchers are beginning to acknowledge the importance of adopting a more multidimensional and integrative approach to the field of motivation (D rnyei, 2000) in order to examine how the wide variety of motivational constructs and theories relate to one another (Murphy & Alexander, 2000; Pintrich, 2003). In a bid to adopt a more holistic approach to motivation and engagement, the current study utilises the Student Motivation and Engagement Wheel, which was developed to integrate a number of theoretical perspectives and articulate a framework that is readily accessible to practitioners, parents, and students. The model comprises eleven facets of motivation and engagement and it is this integrative framework that makes this model successful at capturing the complexity and breath of dimensions that underpin academic motivation and engagement. Although a detailed account of the wheel is beyond the scope of this present investigation, as fully discussed in Martin (2001, 2002, 2003b), there are four major dimensions to the model: adaptive cognitive dimensions, adaptive behavioural dimensions, impeding cognitive dimensions and maladaptive behavioural dimensions. The following is provided as a general overview of the theoretical orientations and associated constructs. The Wheel draws on theory and related research for each of its four main dimensions. The adaptive cognitive dimensions of student motivation encompass (a) self-efficacy (Bandura, 1997), (b) expectancy-value theory to include valuing of school (Eccles, 1983; Wigfield, 1994), and (c) goal theory (viz. mastery orientation) and self-determination theory (viz. intrinsic motivation) to incorporate mastery orientation (Elliott & Dweck, 1988; Kaplan & Maehr, 2002; Nicholls, 1989; Ryan & Deci, 2000). The adaptive behavioural dimension of the Wheel accommodates the following theory and research, (a) choice theory to include persistence (Glasser, 1998), and (b) self-regulation theory to include study management and planning (Zimmerman, 2001). In terms of the impeding and maladaptive dimension the Wheel draws on research and theorizing on (a) anxiety (Sarason & Sarason, 1990; Spielberger, 1985), (b) uncertain control is drawn from control and attribution theories (Connell, 1985; Weiner, 1994), and (c) need achievement theory, goal theory, and self-worth motivation theory together form failure avoidance, self-handicapping and disengagement (Atkinson, 1957; Covington, 1992, 1998; Elliot & Sheldon, 1997; McClelland, 1965). The Student Motivation and Engagement Wheel is presented in Figure 1.

2 ADAPTIVE COGNITIVE DIMENSIONS Valuing of school Persistence ADAPTIVE BEHAVIORAL DIMENSIONS Mastery orientation Planning Selfhandicapping Selfefficacy Study management Disengagement Anxiety Failure avoidance MALADAPTIVE BEHAVIORAL DIMENSIONS Uncertain control IMPEDING AFFECTIVE DIMENSIONS Figure 1 The Student Motivation and Engagement Wheel adapted from Martin, A. (2003a). How to motivate your child for school and beyond. Sydney: Bantam The Student Motivation and Engagement Scale (Martin, 2001, 2003b) has been developed to measure each facet of the Student Motivation and Engagement Wheel. The Scale comprises 44 items in which four items assess each of the eleven facets in the Wheel. Martin (2001, 2002, 2003b) has shown that the Student Motivation and Engagement Scale is a valid and reliable measure of academic motivation and engagement. For example, Martin (2001, 2002, 2003b) used LISREL procedures to confirm a strong factor structure of the Student Motivation and Engagement Scale. He has also shown that the Student Motivation and Engagement Scale is a reliable instrument with approximately normally distributed dimensions. In addition, this scale has been validated and significantly associated with literacy, numeracy, and achievement in mathematics and English as well as being sensitive to age and gender related differences in motivation. The Issue of Domain Specificity of Academic Motivation and Engagement In addition to examining student motivation from a variety of theoretical perspectives, it is also valuable to explore the relative motivational salience of different school subjects in research designs (Bong, 1996; Pintrich, 2003). Until recently, there existed a significant reliance on measures of global academic motivation that reflected an attempt to broadly represent all school subjects. It has often been assumed that student motivation is uniform across school subjects and that a global measure of motivation is sufficient enough to capture the complexity of school motivation and engagement (Bong, 2001; Marsh, Martin & Debus, 2002; Pokay & Blumenfeld, 1990). Contemporary academic motivation research, however, attempts to account for the possibility that academic motivation and engagement may vary as a function of the subject domain. This raises important questions as to whether academic motivation is domain specific or domain general. If it is domain specific then goals and values may vary as a function of the school subject. If it is domain general then there should be little

3 differentiation between motivational dimensions from subject to subject. Therefore, a student may be highly motivated in an English subject but less motivated in mathematic-based subjects (Bong 1996; Marsh et al, 2002; Pintrich, 2003). Recently, an extensive body of literature has shown support for the usefulness of studying the domain specificity of various motivational constructs. For instance, Bong (2001) examined the between-domain relation of task value, self-efficacy and achievement goals across four subjects in Korean middle and high school students. She found that students achievement goals were highly correlated across domains; however, taskvalue and mastery goals were more distinct across domains. Duda and Nicholls (1992) also wrestled with this issue when they found that there was high subject generality of goal orientations and beliefs about the causes of success, whilst perceptions of ability and intrinsic satisfaction across schoolwork and sport were domain specific. Moreover, previous research has found evidence of the domain specificity of students valuing of and interests in various subjects. Students appear to experience a decline in valuing of mathematics after the junior high school transition, whereas their valuing of English increases (Wigfield, Eccles, MacIver, Reuman & Midgley, 1991). Conversely, an investigation into elementary students interest for various school subjects showed that children experience a decline in interest for reading and instrumental music, but their interest in sports and mathematics did not change over the course of the three-year study (Wigfield, Eccles,Yoon, Harold, Arbeton et al., 1997). In relation to other motivational dimensions, Gottfried (1982) measured anxiety and intrinsic motivation in four school subjects (reading, mathematics, social studies, and science) and concluded that the relationship between academic intrinsic motivation and anxiety varied according to the school subject. Smith and Fouad (1999) also confirmed the existence of different levels of self-efficacy, interest, outcome expectancies and goals for mathematics, art, social science and English subjects. In particular, motivational researchers (e.g., Eccles, Wigfield, Harold & Blumfeld., 1993) have emphasised the domain specificity of constructs such as expectancy for success (defined in terms of perceived competency, anxiety, and self-concept) and task value (defined in terms of interest, usefulness, and challenge). Support for the domain specificity of academic affect is judged to be clearest in research focusing on selfconcept, which has predominately, echoed the need to explore this issue of domain specificity of motivational constructs. In early research, Marsh, Byrne, and Shavelson (1988) found that correlations between mathematic and English self-concepts based on each of three different instruments utilised were close to zero. Marsh and Craven (1997; see also Marsh, 1993; 1990) integrated a growing body of research showing that verbal and mathematic self-concepts are nearly uncorrelated and that the effects of academic self-concept on subsequent outcomes are also very specific to the subject domain. Interestingly, subsequent research with math and verbal self-efficacy measures has not shown consistent findings. In fact, math and verbal self-efficacy were found to be substantially correlated whilst math and verbal self-concept was uncorrelated (Marsh, Walker, & Debus, 1991). More recently, research conducted by Marsh, Martin and Debus (2002), found similar results. They discovered that there was extreme content specificity for self-concept but that this was not the case for constructs such as self-handicapping and external attributions, which obtained the largest math-verbal correlations. Despite these recent advancements in research of the domain specificity of academic motivation and engagement, inconsistent findings persist and a comprehensive and integrative framework continues to be lacking. In sum, the research reviewed here has highlighted the need for future academic motivation research to account for the possibility that academic motivation and engagement may vary as a function of the subject domain. A large majority of the research reviewed here employs research designs in which students rate target dimensions of motivation and engagement for separate school subjects in one testing session. Given that this methodology may affect an individual s rating of motivation (Roeser, Midgley, & Urdan, 1996; see also Midgely & Urdan, 1995; Urdan, Midgley & Anderman, 1998), it is important to conduct such research in the context of the class to which those ratings relate. Accordingly, the present investigation attempts to examine the issue of domain specificity by asking students to rate their motivation and engagement in the actual subject to which those ratings relate (i.e., mathematics ratings in the mathematics class; English ratings in the English class, etc.). Furthermore, because motivation research has typically been a diffuse activity for the past few decades (Murphy & Alexander, 2000;

4 Pintrich, 2003), it is necessary to conduct this domain specificity research in the context of an integrative framework. The Present Investigation Although the aims of the present investigation are multi-fold, the primary aim is to establish the extent to which the motivational constructs of the SMES are differentiated on the basis of school subject. Using a recently developed model and instrument representing student motivation and engagement, three major subject domains are selected to investigate this issue: mathematics, English and science. As mentioned earlier, most of the research reviewed here employs a research design that requires students to rate their motivation and engagement for various school subjects in one class only (typically mathematics). The present investigation adopts a more ecologically valid (see Bronfenbrenner, 1979) way of assessing selfreports of motivation and engagement in specific subjects by asking students to rate their motivation while they are in the specific class to which these ratings relate. An additional aim of the study is to explore gender and grade (and their interaction) related difference in motivation and engagement. This assessment will allow for the investigation of the generality of the Student Motivation and Engagement Wheel as well as the specific ways in which males and females of different ages respond to the SMES in different school subjects. Method Participants and Procedure Respondents were 1,801 students from six Australian government high schools in Years 7 and 8 (52% - junior high), Years 9 and 10 (40% - middle high), and Years 11 and 12 (8% - senior high). All schools were located in urban areas of Canberra and Sydney and schools primarily drew on middle class areas. 183 classes were administered the questionnaire and 94 teachers participated in the administration. Specifically, there were 1,412 respondents for mathematics, 756 respondents for English and 841 respondents for science. A larger response rate was obtained for mathematics because schools were given the option of administering the questionnaire to a mathematics class and one other subject (either English or science). In total, 33% of students were females and 67% males. The sample comprises a higher percentage of male students because of the inclusion of a single sex boys school. This particular school also administered the survey to years 11 and 12 whereas the remaining co-educational schools only administered the instrument to junior and middle high students. The mean age of students was 14.4 years (SD = 1.4). Students were asked to rate their motivation in mathematics, English, and science and did so in at least two of these three subjects. Students rated their motivation of a school subject in the actual class where they studied that subject. The teacher first explained the rating scale and a sample item presented. Students were then asked to complete the instrument on their own and to return it to the teacher at the end of the class once the survey was completed. Measures Students completed the Student Motivation and Engagement Scale (Martin, 2001, 2003b) which is an instrument that measures high school students motivation. It is hypothesized to assess motivation through three adaptive cognitive dimensions, three adaptive behavioural dimensions, three impeding cognitive dimensions, and two maladaptive behavioural dimensions of motivation and engagement. Each of the eleven factors comprises four items hence it is a 44-item instrument. To each item, students rate themselves on a scale of 1 ( Strongly Disagree ) to 7 ( Strongly Agree ).

5 Adaptive dimensions of motivation and engagement Each adaptive dimension falls into one of two groups: cognitions and behaviours. Adaptive cognitions include self-efficacy, mastery orientation, and value of schooling. Adaptive behaviours include persistence, planning, and study management. Self-efficacy (eg. "If I try hard, I believe I can do my schoolwork well"): Adapted in part from Midgley et al s (1997) Patterns of Adaptive Learning Survey, self-efficacy is students belief and confidence in their ability to understand or to do well in their schoolwork, to meet challenges they face, and to perform to the best of their ability. Valuing of subject (eg. "Learning in this subject is important to me"): Adapted in part from Pintrich, Smith, Garcia & McKeachie s (1991) Motivated Strategies for Learning Questionnaire, valuing of subject is how much students believe what they learn in the subject is useful, important, and relevant to them or to the world in general. Mastery orientation (eg. "I feel very pleased with myself when I really understand what I m taught at school"): Adapted in part from Nicholls (1989), mastery orientation is being focused on learning, solving problems, and developing skills. Planning (eg. "Before I start an assignment I plan out how I am going to do it"): Adapted in part from Miller et al. (1996), planning is how much students plan their schoolwork, assignments, and study and how much they keep track of their progress as they are doing them. Study management (eg. When I study, I usually study in places where I can concentrate ): Adapted in part from Pintrich et al. (1991), study management refers to the way students use their study time, organize their study timetable, and choose and arrange where they study. Persistence (eg. "If I can t understand my schoolwork at first, I keep going over it until I understand it"): Adapted in part from Miller et al. (1996), persistence is how much students keep trying to work out an answer or to understand a problem even when that problem is difficult or is challenging. Impeding cognitive-affective dimensions Impeding cognitive-affective dimensions are anxiety, failure avoidance, and uncertain control. Anxiety (eg. "When exams and assignments are coming up, I worry a lot"): Adapted in part from Pintrich and DeGroot (1990), anxiety has two parts: feeling nervous and worrying. Feeling nervous is the uneasy or sick feeling students get when they think about their schoolwork, assignments, or exams. Worrying is their fear about not doing very well in their schoolwork, assignments, or exams. Failure avoidance (eg. "Often the main reason I work at school is because I don t want to disappoint my parents"): Adapted from an orientation outlined by Harter, Whitesell, and Kowalski (1992), students have an avoidance focus when the main reason they do their schoolwork is to avoid doing poorly or to avoid being seen to do poorly. Uncertain control (eg. "I'm often unsure how I can avoid doing poorly at school"): Adapted in part from Connell s (1985) Unknown cognitive dimension of the Multidimensional Measure of Children s Perceptions of Control (1985), this subscale assesses students uncertainty about how to do well or how to avoid doing poorly. Maladaptive behavioural dimensions Maladaptive behavioural dimensions are self-handicapping and disengagement. Self-handicapping (eg. "I sometimes don t study very hard before exams so I have an excuse if I don t do as well as I hoped"): Adapted from the Academic Self-Handicapping Scale (Midgley, Arunkumar, & Urdan, 1996) and the Shortened Self-handicapping Scale (Strube, 1986), students self handicap when they do things that reduce their chances of success at school. Examples are putting off doing an assignment or wasting time while they are meant to be doing their schoolwork or studying for an exam. Disengagement (eg. "I often feel like giving up at school"): Students are disengaged or at risk of disengagement when they feel like giving up in particular school subjects or school generally. Students high in disengagement tend to accept failure and behave in ways that reflect helplessness.

6 Confirmatory Factor Analysis Statistical Analyses Confirmatory factor analysis (CFA), performed with LISREL version 8.54 (Joreskog & Sorbom, 2003), is the primary method used to test the psychometric properties of the Student Motivation and Engagement Scale. This statistical technique allows the researcher to test whether theoretically derived relationships actually manifest in the data as well as allowing for the systematic comparison of different proposed models. In CFA, the researcher hypothesizes a model that is assumed to describe or account for the data in terms of relatively few parameters. An a priori structure is proposed and the researcher is able to test the ability of a model based on this structure to fit the data by demonstrating the following, the parameter estimates are consistent with theory and a priori predictions; the solution is well defined, and the 2 and subjective indices of fit are reasonable (Marsh, Balla & McDonald, 1988; McDonald & Marsh, 1990). The Student Motivation and Engagement Scale constructs are measured using parallel items for each subject. In the case of parallel items, measurement errors associated with matching items across subjects are likely to be correlated. Failure to take into account these correlations between measurement errors (hereafter referred to as correlated uniquenesses ) may result in biased parameter estimates and the relation between latent constructs will be positively biased (e.g., Marsh, Balla & Hau, 1996). Therefore, it is recommended that proposed models include these correlations among uniquenesses in order to obtain an accurate estimate of relations among constructs (Joreskog, 1979; Marsh, Roche, Pajares & Miller, 1997). The present study has attempted to incorporate such correlated uniquenesses by correlating the residuals of parallel items. It is increasingly recognised that conventional methods of handling missing data such as listwise or pairwise deletion are generally weak methods (Enders, 2001; Allison, 2003). A growing body of research suggests that full information maximum likelihood (FIML) parameter estimates are substantially more efficient and uniformly more accurate than traditional methods (Duncan, Duncan & Li, 1998; Enders, 2001). FIML as operationalised using missing value analysis in LISREL, is the primary method of handling missing data in the CFA of this investigation. In evaluating goodness of fit of alternative models, the non-normed fit index (NNFI) and comparative fit index (CFI) are usually presented when conventional methods are used for handling missing data. In the case of FIML these fit indices are not available in LISREL, hence the focus will be on the widely endorsed criterion of fit, root mean square error of approximation (RMSEA) as well as the ² test statistic. For RMSEAs, values at or less than.08 and.05 are taken to reflect an acceptably close fit and an excellent fit respectively (see Joreskog & Sorbom, 1993; Marsh, Balla & Hau, 1996; Schumacker & Lomax, 1996). Although tests of statistical significance and indices of fit assist in the evaluation of the best fit, it is important to note that a degree of subjectivity and professional judgment in the selection of a best model does exist. Multiple-Indicator-Multiple-Cause (MIMIC) Models As mentioned earlier, the current study aims to investigate the effects of gender and grade on the eleven facets of the SMES. Kaplan (2000; see also Grayson, Mackinnon, Jorm, Creasey & Broe, 2000) suggested the MIMIC approach, is similar to a regression model in which latent variables (e.g., multiple dimensions of student motivation and engagement) are caused by discrete grouping variables (e.g., gender, grade, gender x grade) that are represented by a single indicator. However, one advantage of the MIMIC approach over the standard approach is that it can handle cases in which sample size in a given group may be too small to ensure stable estimates of variances and covariances. Moreover, by representing group membership in appropriate ways, the MIMIC approach allows the researcher to consider more familiar models of main effects and interactions. This type of model also has the important advantage in that the dependent variables are latent variables based on multiple indicators. The present MIMIC model included the effects of gender, grade (treated as a continuous variable) and the gender x grade interaction. Consistent with recommendations made by Aiken and West (1991), grade was zero-centered (put in deviation score form so that the mean is zero) so as to reduce the multicollinearity between grade and the corresponding interaction term. Very high levels of multicollinearity can introduce technical problems in estimating regression coefficients and centering variables often minimises these potential problems.

7 The zero-centered interaction term was calculated using gender (1=Girls, 2=Boy) by the zero-centered grade variable. In evaluating goodness of fit for each MIMIC model, the root mean square error of approximation (RMSEA) is presented as well as the non-normed fit index (NNFI), the comparative fit index (CFI) and the ² test statistic. CFI and NNFI indices were available for the MIMIC models because they were subject specific and so did not employ FIML procedures to handle missing data. An evaluation of parameter estimates is also provided. For RMSEAs, values at or less than.05 are taken to reflect a close fit (see Marsh et al., 1996; Schumaker & Lomax, 1996). The NNFI and CFI vary along a continuum in which values at or greater than.90 are typically taken to reflect acceptable fits to data (McDonald & Marsh, 1990). The CFI contains no penalty for lack of parsimony so that improved fit due to the introduction of additional parameters may reflect capitalisation on chance, whereas the NNFI and RMSEA contain penalties for lack of parsimony. Once again it must be noted that significance and indices of fit only aid in the evaluation of model and so ultimately a degree of subjectivity and professional judgement is required in the selection of the best fitting model. Results Confirmatory Factor Analysis Three proposed models are of particular focus in the present study. The first model is a 33-factor model in which the 11 facets of the Student Motivation and Engagement Scale were freely estimated in each of the three subjects. The second proposed model is an 11-factor model, whereby maths, science and English items jointly load on each of the 11 factors. The final model is a three-factor model where all of the items are collapsed into each subject. CFA was conducted for each alternate model and the recommended fit indices are presented in Table 1. Although each model generated acceptable fits to the data, as can be seen in Table 1, the 33-factor model yielded the best fit ( ² = , df = 7854, RMSEA =.025). Table 1. Goodness of fit indicators of models for the Student Motivation and Engagement Scale FIML ² Degrees of Freedom RMSEA 33-factor model 16, factor model 24, factor model 38, The 33-factor Model Parameters Factor loadings for this 33-factor model are presented in Table 2. acceptable. Taken together the loadings are

8 Table 2. Factor loadings for the 33-factor model (maths/science/english) Self effic (SE) Mast orient (MO) Value school (VS) Plan (PLN) Study man (SM) Persist (P) Anxiety (ANX) Failure avoid (FA) Uncert control (UC) Selfhandi (SH) Disenga (D) SE1 70/71/78 SE2 69/66/72 SE3 65/65/67 SE4 73/72/74 MO1 67/67/73 MO2 71/77/73 MO3 76/76/73 MO4 81/78/78 VS1 58/59/70 VS2 74/71/71 VS3 75/68/78 VS4 68/69/71 PLN1 65/59/71 PLN2 78/76/83 PLN3 78/83/78 PLN4 43/44/46 SM1 71/71/77 SM2 71/72/74 SM3 82/85/81 SM4 72/73/75 P1 61/63/67 P2 72/75/77 P3 73/74/81 P4 77/77/80 ANX1 77/80/82 ANX2 70/74/70 ANX3 54/64/62 ANX4 69/65/70 AV1 80/79/84 AV2 82/84/80 AV3 47/55/59 AV4 60/68/59 UC1 65/60/65 UC2 66/65/74 UC3 74/71/74 UC4 74/75/74 SH1 64/65/74 SH2 79/76/77 SH3 77/77/73 SH4 73/7576/ D1 63/61/71 D2 70/63/75 D3 72/67/74 D4 79/79/79 Note: Decimals omitted and order of coefficient is as follows: mathematics/ science/ English. SE = Self-efficacy, MO = Mastery orientation, VS = Value of subject, PLN = Planning, SM = Study manage, P = Persistence, A = Anxiety, FA = Failure avoid, UC = Uncertain control, SH = Self-handicapping, D = Disengagement.

9 Within-subject correlations emerging from the CFA for the 33-factor model are shown in Table 3. As in previous research, the adaptive cognitive constructs are highly correlated with each other but less correlated with the adaptive behavioural dimension. Similarly, there are moderate correlations within the impeding cognitive dimension and these are less correlated with the maladaptive behavioural dimensions. It can be seen that the impeding and maladaptive dimensions are negatively correlated with the two adaptive dimensions. These within-subject correlational patterns appear to be consistent across subjects. Table 3. Within-subject correlations emerging from CFA (maths/science/english) for the 33-factor model SE MO VS PLN SM P A FA UC SH D SE - MO 77/75/78 - VS 78/74/81 75/78/83 - PLN 52/48/56 49/50/52 52/43/51 - SM 54/54/57 54/57/63 49/55/52 74/74/78 - P 72/67/73 64/66/70 67/66/71 68/61/71 66/64/68 - A 12/11/01 30/33/18 15/23/01 11/11/11 19/18/16 14/15/06 - FA -21/-17/-26-08/-07/-15-12/-07/-23-02/-04/-11-01/-03/-05-11/-06/-19 42/46/50 - UC -26/-34/-35-05/-10/-16-13/-18/-23-12/-19/-15-02/-16/-09-25/-31/-29 49/46/52 54/53/61 - SH -41/-38/-47-33/-29/-34-34/-33/-39-23/-25/-23-25/-28/-22-41/-43/-43 15/17/27 49/51/49 45/57/53 - D -64/-57/-60-59/-53/-58-71/-64/-69-45/-43/-36-44/-47/-38-66/-61/-62 03/08/14 38/40/38 40/49/48 60/62/62 - Note. Decimals omitted and order of correlations is as follows: mathematics / science/ English SE = Self-efficacy, MO = Mastery orientation, VS = Value of subject, PLN = Planning, SM = Study manage, P = Persistence, A = Anxiety, FA = Failure avoid, UC = Uncertain control, SH = Self-handicapping, D = Disengagement. Of most interest are the between-subject correlations, which are presented in Table 4. These results show correlations range between.62 and.72 suggesting that although constructs share considerable variance across subjects the correlations are not so high that they are essentially measuring the same construct. Also, for different dimensions of motivation there can be quite different correlational patterns. For example, valuing of school appears to have relatively lower correlations (e.g., r =.52 to r =.62) suggesting that students valuing of school subject is not necessarily reflected in their valuing of another subject. Conversely, study management and anxiety evidence relatively higher parallel correlations (e.g., r =.86 for anxiety). These results suggest that students performance anxiety and study management are more similar across maths, English and science high school subjects. That is, study management and anxiety appear to be relatively more domain general. A more global view of domain specificity derived through average correlations for the same scale across different subjects yields an average correlation coefficient of r =.72, which although reasonably high, leaves approximately half the variance in relationships between parallel constructs unaccounted for suggestive of modest domain specificity.

10 Table 4. Between -subject correlations emerging from CFA for the 33-factor model SE MO VS PLN SM P A FA UC SH D SE 71/72/72 MO 51/46/56 69/64/75 VS 50/49/52 48/52/54 53/52/62 PLN 41/35/45 40/31/42 27/32/44 78/73/79 SM 38/43/46 39/41/49 37/42/44 57/57/59 83/73/79 P 46/47/54 48/50/56 40/44/52 50/45/55 50/40/47 69/63/75 A 15/-01/05 28/15/24 18/14/19 13/02/18 18/09/17 17/07/12 86/85/79 FA -05/-15/ /-10 /-05-02/-15/ /-26/ /-33/ /-04/ /-12 /-12-17/-30/ / /-18/ /-20/ /-28/ /-30/ /00/00-04/-08/ /-24/ /-34/ /-33/ /36/31 74/73/69 UC -17/-24/ -08/-11/ /33/32 46/43/45 77/84/69 SH -23/-44/ -20/-18/ /13/10 37/41/37 36/52/37 75/78/74 D -39/-34/ -33/-27/ /02/00 29/25/20 37/37/25 46/43/44 63/59/60 Note. Decimals omitted and order of correlations is as follows: mathematics science/ science-english / mathematics - English; Parallel correlations are in bold. SE = Self-efficacy, MO = Mastery orientation, VS = Value of subject, PLN = Planning, SM = Study manage, P = Persistence, A = Anxiety, FA = Failure avoid, UC = Uncertain control, SH = Self-handicapping, D = Disengagement. Gender and Grade Differences for Each of the SMES Factors Three MIMIC models (mathematics, English and science) were developed to examine the relationship between gender, grade and the gender x grade interaction for each of the eleven facets of the SMES. The derived fit indices for the three models are as follows, Mathematics ( ² = , df = 946, RMSEA =.045, NNFI =.97, CFI =.97), English ( ² = , df = 946, RMSEA =.048, NNFI =.97, CFI =.97), and Science ( ² = , df = 946, RMSEA =.048, NNFI =.96, CFI =.97). These results show that each of the three MIMIC models adequately fit the data. Derived beta coefficients are presented in Table 5 and significant main effects for gender and grade as well as the significant interaction effects for each subject are indicated. Although analyses were conducted on all students who participated in the study, it will be recalled that only males were administered the instrument for the senior year-levels therefore no female reference point is available to aid in interpretation. For this reason, minimal attention is given to the interpretation of senior high school findings and the substantive focus of significant interaction effects is on the junior and middle high groups in which both boys and girls were present. Nonetheless, a general finding for male students in senior high school is that motivation and engagement continues to decline. One exception was found for English whereby boys study management increased.

11 Table 5 Standardised Beta Coefficients for Gender, Grade and Gender x Grade Interactions for Mathematics, English and Science High School Subjects Maths English Science Grade x Gender Grade Gender Grade Gender Grade x Gender Grade Gender Grade x Gender Self-efficacy * -.31* * Mastery orientation -.13*.31* * * Value of subject * * -.41*.32 Planning *.56* Study manage -.06* * Persistence -.08* * Anxiety -.29*.43* -.27* -.17* * Failure avoidance Uncertain control Self-handicapping.07* -.37*.36* * -.41* Disengagement * * Note. Mathematics (n = 1406), English (n = 750), Science (n = 836). Girls = 1 and Boys = 2. * p <.05.

12 Main Effects The extent to which gender differences emerge for each of the 11 facets of the SMES in the three high school subjects was under investigation in the present study. Specifically, for mathematics girls were higher on: mastery orientation, study management, persistence, anxiety and lower on self-handicapping; for science, girls were higher on self-efficacy, mastery orientation, valuing of subject, persistence and anxiety and lower on disengagement; for English, girls were higher on mastery orientation and anxiety. It is important to recognise, however, that the main effects for mathematics anxiety, mathematics selfhandicapping and science self-efficacy were qualified by an interaction effect. Grade differences for each of the SMES factors in each of the three subjects were also of interest. Significant main effects for grade were found for mathematics mastery orientation, self-efficacy, anxiety and self-handicapping as well as English disengagement and planning. Science valuing of subject and self-handicapping also reached significance. These main effects were such that, upper year-levels evinced higher scores. The findings for science self-handicapping and English planning however were qualified by an interaction effect. Gender x Grade Interaction MIMIC models also allowed for the investigation of the possible gender x grade interaction effects. The mathematic MIMIC model revealed four significant interaction effects for self-efficacy, valuing of subject, anxiety and self-handicapping (see Figure 2a through to Figure 2g). A trend across each significant interaction is that boys and girls appear to have almost similar ratings at the junior level but by middle school the ratings diverge. In terms of self-efficacy, girls in the middle year-level have higher selfefficacy ratings (M = 79.76) than boys (M = 77.66). A similar pattern can be seen for valuing of subject construct. By middle high males value school less (M = 76.95) than females (M = 78.04). The interaction effect for mathematic anxiety shows that in middle school females experience a larger rise in anxiety (M = 69.42) than males (M = 56.50). The mathematic self-handicapping interaction indicates that by middle school girls report lower ratings of self-handicapping techniques (M = 39.68) than boys (M = 43.93). For mathematics the general interaction pattern is that girls are more motivated in middle high but at the same time are also more anxious than boys. For English, interactions for planning and study management reached significance (see Figure 6 and Figure 7). Once again males and females ratings in junior high for each of the significant interactions are similar but by the middle year-level, girls experience a larger drop in planning (M = 53.57) than boys (M = 60.49). Girls study management for English also tends to drop by middle school (M = 60.40) whereas boys report an increase in study management in middle school (M = 66.47). This is an interesting juxtaposition to the mathematics findings, which showed higher motivation (but on different dimensions) in middle high school. This is yet another finding that suggests distinctiveness across subjects and domain specificity. For science, a significant gender by grade interaction was found for the self-handicapping construct (see Figure 8). A different pattern can be seen for junior high in that males experience higher selfhandicapping (M = 42.24) than females (M = 37.20). By the time the students reach middle school, males (M = 43.17) and females (M = 42.78) report similar self-handicapping use.

13 Mean Junior Middle Senior Year Level Female Male Mean Junior Middle Senior Year Level Female Male Figure 2a. Mathematics gender x grade interaction on selfefficacy. Figure 2b. Mathematics gender x grade interaction for value of subject Mean Female Male Mean Female Male 0 Junior Middle Senior 10 Junior Middle Senior Year Level Year Level Figure 2c. Mathematics gender x grade interaction for anxiety Figure 2d. Mathematics gender x grade interaction for Self-handicapping Mean Female Male Mean Female Male 45 Junior Middle Senior Year Level 55 Junior Middle Senior Year Level Figure 2e. English gender x grade interaction for planning Figure 2f. English gender x grade interaction for study management Mean Junior Middle Senior Year Level Female Male Figure 2g. Science gender x grade interaction for Selfhandicapping

14 Discussion Although substantial research underpins the field of motivation and engagement, contemporary research has only recently begun to appreciate the complex nature of domain specificity. The present investigation evaluated the need to distinguish between mathematic, science and English academic motivation and engagement. Consistent with a priori predictions, CFA models with correlated uniquenesses confirmed that the domain specific 33-factor model was the best fitting one. Substantively, the results show that there is in fact distinctiveness between math, science and English subjects however the level of difference varies according to the particular academic motivation construct. For example, between-subject correlations for valuing were relatively low, however, correlations for anxiety appeared to be more domain general, More importantly, this study has demonstrated that it would be inappropriate to collapse these constructs across school subjects to form a global or subject-general view of student motivation. In addition to these domain specific findings, the possible emergence of gender and grade differences for each of the SMES factors across subjects was also under investigation. Essentially, where significant effects for gender occur, girls are more motivated than boys with the exception of anxiety, irrespective of school subject. In broad terms, significant main effects of grade showed that higher year levels experience an increase in maladaptive dimensions across each of the three school subjects. For mathematics however an increase in mastery orientation and self-efficacy were found for higher year levels. Few significant gender x grade interactions were revealed, with most of them occurring for mathematics. Of these few interactions it appears as though girls are more motivated in mathematics as they move to middle high school, but in English, experience a decline in planning and study management. In science girls experience an increase in self-handicapping. These diverse patterns of interactions across subjects are further evidence of domain specificity. The results from this research are noteworthy because previous research has not been approached from the perspective of an integrated framework. Hence, it can be argued that the current study has been successful in harnessing a recently developed integrative model of student motivation and engagement to shed light on the important issue of domain specificity. This research is also powerful because the present study has employed a unique methodology; one in which students rated the subject (math, science or English) within the actual class. The results of this study have important implications for applied research and practise because the theoretically derived constructs within the Wheel allow researchers and practitioners to draw on theory to provide direction for intervention aimed at enhancing students motivation and engagement. Findings suggest that targeted intervention will be more effective than domain general intervention (see Weisz, Weiss, Han, Granger, & Morton, 1995) in that it is possible to assess the extent to which motivation and engagement varies as a function of school subjects. The present study provides an expansive model of student motivation and engagement, however, a number of potential limitations need to be considered when interpreting these findings. Firstly, the data presented in this study are of self-report nature. Although self-report measures are a justifiable methodology, it is imperative that future research examines these same constructs in subjects using data that is derived from additional resources such as teachers and parents. There is a significant reliance on self-report measures in the literature reviewed and different modes of inquiry are needed (Bong, 1996; Pintrich, 2000). Additionally, this research is one-shot in nature. Employment of longitudinal research has the potential to clarify and uncover the possible motivational fluctuations across time (Bong, 1996; Jacob et al, 2002). In fact, Pokay and Blumenfeld (1990) suggest that there is evidence that motivation and achievement change as a function of time. The current research encompassed cross-sectional data and more far-reaching conclusions regarding the domain specificity of student motivation await a longitudinal investigation that utilises additional modes of enquiry. Notwithstanding these limitations, the present study has provided support for the domain specificity of motivation in the factors underpinning the SMES. Additional findings of gender and grade level differences for each subject lend further support for the domain specificity of motivation and engagement. Taken together, the findings of the present investigation hold not only substantive and methodological implications for researchers studying motivation and engagement, but they are also relevant to educators operating in contexts in which the specific demands of different school subjects need to be recognised and appropriately accommodated.

15 About the Authors Jasmine Green is a psychology Doctoral candidate with the SELF Research Centre. In 2004, Jasmine completed the fourth year of her BPsych (Hons) program at the University of Western Sydney under the supervision of Professor Marsh and Dr Andrew Martin. Utilising a longitudinal design, her current research will examine the transitional changes associated with motivation and engagement and the causal ordering of various motivational variables. She is a member of the Golden Key Honour Society, as well as the current recipient of the prestigious Australian Postgraduate Award. Dr Andrew Martin is Post Doctoral Research Fellow at the SELF Research Centre, University of Western Sydney. He is a Registered Psychologist and specialises in student motivation. In 2003, he was named in The Bulletin s Smart 100 Australians and in the Top 10 in the field of Education. In 2002, his PhD was judged the Most Outstanding Doctoral Dissertation in Educational Psychology by the American Psychological Association and before that was judged the Most Outstanding PhD in Education in Australia by the Australian Association for Research in Education. Professor Herb Marsh is Professor of Educational Psychology, founding Director of the SELF Research Centre and served as UWS s inaugural Dean of Graduate Research Studies and Pro-Vice-Chancellor. He received UWS s inaugural awards for Research, Postgraduate Supervision, and Doctorate of Science. Herb has published more than 250 peer-reviewed journal articles, 40 chapters, 10 monographs, and 225 conference papers. He is Australia s most widely cited researcher in both education and psychology, and the 11 th mostly widely cited researcher in the world across all disciplines of psychology. Contact Details Jasmine Green PhD Candidate SELF Research Centre Bankstown Campus, Penrith South NSW 1797 Australia ja.green@uws.edu.au Phone: (02) (or ) Fax: (02) (or ) Dr Andrew Martin Postdoctoral Research Fellow SELF Research Centre University of Western Sydney Bankstown Campus, Penrith South NSW 1797 Australia a.martin@uws.edu.au Phone: (02) (or ) Fax: (02) (or ) Professor Herb Marsh Director, SELF Research Centre University of Western Sydney Bankstown Campus, Penrith South NSW 1797 Australia h.marsh@uws.edu.au Phone: (02) (or ) Fax: (02) (or ) References Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. California: SAGE Publications, Inc. Allison, P. D. (2003). Missing data techniques for structural equation modelling. Journal of Abnormal Psychology, 112(4), Atkinson, J. W. (1957). Motivational determinants of risk taking. Psychological Review, 64, Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman & Co.

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