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Exploring students’ reflectivethinking practice, deep processingstrategies, effort, and achievementgoal orientationsHuy Phuong Phan aa School of Education , University of the South Pacific , LaucalaCampus, Suva, FijiPublished online: 18 May 2009.

To cite this article: Huy Phuong Phan (2009) Exploring students’ reflective thinking practice,deep processing strategies, effort, and achievement goal orientations, Educational Psychology:An International Journal of Experimental Educational Psychology, 29:3, 297-313, DOI:10.1080/01443410902877988

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Educational PsychologyVol. 29, No. 3, May 2009, 297–313

ISSN 0144-3410 print/ISSN 1469-5820 online© 2009 Taylor & FrancisDOI: 10.1080/01443410902877988http://www.informaworld.com

Exploring students’ reflective thinking practice, deep processing strategies, effort, and achievement goal orientations

Huy Phuong Phan*

School of Education, University of the South Pacific, Laucala Campus, Suva, FijiTaylor and FrancisCEDP_A_387970.sgm10.1080/01443410902877988(Received 7 March 2008; final version received 9 March 2009)Educational Psychology0144-3410 (print)/1469-5820 (online)Research Article2009Taylor & Francis00000000002009Dr. HuyPhanhphan2@une.edu.au

Recent research indicates that study processing strategies, effort, reflectivethinking practice, and achievement goals are important factors contributing to theprediction of students’ academic success. Very few studies have combined thesetheoretical orientations within one conceptual model. This study tested aconceptual model that included, in particular, deep processing strategies, effort,mastery and performance-approach goals, reflection, and critical thinking. Weused causal modelling procedures to explore the direct and mediating effects ofthese theoretical orientations on students’ academic achievement and learning.Second- and third-year undergraduates (n = 347; 151 women and 196 men)completed a number of inventories (e.g., the Reflective Thinking Questionnaire).LISREL 8.72 indicated an a posteriori model with direct effects of reflection andcritical thinking on academic achievement and learning. Performance-approachgoals exerted a negative effect on academic achievement. Both mastery andperformance-approach goals also directed affected reflection, whereas deepprocessing strategies were directly affected by mastery goals and effort.Importantly, both reflection and effort were found to act as potent mediators. Aone-way MANOVA revealed no statistical difference between men and women inthis theoretical framework.

Keywords: reflective thinking; study strategies; achievement goals; effort;academic success

Introduction

An emerging research interest in educational psychology concerns how differenttheoretical frameworks may combine to explain the success of academic learning.Empirical research has provided evidence to show how achievement goals act inconcert with study processing strategies and reflective thinking practice to predictstudents’ academic performance (e.g., Fenollar, Román, & Cuestas, 2007; Phan,2008a; Simons, Dewitte, & Lens, 2004). The present study, based on existingresearch, attempts to explore within one conceptual framework the relationshipsbetween six key motivational variables (mastery and performance-approach goalorientations, deep processing strategies, effort, reflection, and critical thinking) andacademic performance (denoted by both academic achievement and learning). Specif-ically, the research questions addressed in this study are:

*Email: hphan2@une.edu.au

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RQ1: What are the positive effects of mastery and performance-approach goals,effort, deep processing strategies, and reflective thinking practice on students’academic performance?

RQ2: What is the contribution of effort to deep processing strategies and reflectivethinking practice?

RQ3: What is the contribution of deep processing strategies to reflective thinkingpractice?

RQ4: Are there gender differences in the theoretical framework and in academicperformance?

Theoretical framework: Goal orientation, study processing strategies, and reflective thinking practice

The conceptual framework established in this study is based on previous theoreticaland empirical evidence pertaining to goal orientations (Ames & Archer, 1988; Valle,Cabanach, & Núnez, 2003), study processing strategies (Dupeyrat & Mariné, 2005;Elliot, McGregor, & Gable, 1999; Entwistle & Tait, 1994; Tait & Entwistle, 1996),effort (Elliot et al., 1999; Fenollar et al., 2007), and reflective thinking practice (Leung& Kember, 2003; Mezirow, 1991, 1998). More recently, a few research studies haveamalgamated these individual lines of inquiry within one conceptual framework(Fenollar et al., 2007; Phan, 2007, 2008a). Analysis of the evidence suggests, forexample, that mastery and performance-approach goal orientations, deep studystrategies, effort, and reflective thinking practice act in concert to affect the success ofstudents’ academic learning.

Achievement goal theory has emerged over the past several decades as an importantframework for studying academic motivation and achievement (Shih, 2005). Withinthe last couple of years, empirical research has provided evidence supporting a trichot-omous framework of achievement goals (Elliot, 1997; Elliot & Church, 1997; Elliot& Harackiewicz, 1996). This theoretical framework emphasises three types of goals:mastery goals, performance-approach goals, and performance-avoidance goals.Students who adopt mastery goals are interested in acquiring new skills and improvingtheir knowledge even in the face of obstacles. Those with performance-approach goals,in contrast, strive to demonstrate normatively high ability, whereas students withperformance-avoidance goals avoid normative incompetence. Each type of goalencompasses specific patterns of cognition, affect, and behaviour. Mastery goals, forexample, are related to positive learning behaviours such as a preference for challeng-ing work (Ames & Archer, 1988; Elliot & Dweck, 1988), persistence in the face ofsetbacks (Elliot & Dweck, 1988), intrinsic motivation for learning (Meece, Blumenfeld,& Hoyle, 1988; Stipek & Kowalski, 1989), and the use of deep processing strategies(Ames & Archer, 1998; Meece et al., 1988; Nolen & Haladyns, 1990). Performance-approach goals have been shown to relate to a number of adaptive learning behaviourssuch as high aspiration, absorption during task engagement, and high academicperformance (Elliot, 1999; Elliot et al., 1999). In contrast, performance-avoidancegoals are related negatively to intrinsic motivation (Elliot & Harackiewicz, 1996), andpositively to an unwillingness to seek help, poor academic performance, and the useof surface processing strategies (Elliot, 1999; Elliot & Church, 1997).

The original work of Marton and Säljö (1976) indicates two main approaches tolearning: deep and surface. This theoretical framework suggests that students adopt adeep approach to learning when the main intention is to seek understanding from theauthor’s meaning and relate this to prior knowledge and personal experience. A

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surface approach to learning, in contrast, involves engaging in learning simply for theintention of reproducing information without any detailed or further analysis.Entwistle and Tait (Entwistle, 1997; Entwistle & Tait, 1994; Tait & Entwistle, 1996)have recently expanded this theoretical framework to include deep and surfaceprocessing strategies in learning (Fenollar et al., 2007; Phan, 2008a). Subsequent stud-ies of learning approaches and strategies have, similarly, referred to deep and surfacestudy strategies (DeBacker & Crowson, 2006; Dupeyrat & Mariné, 2005; Simonset al., 2004). Findings indicate, in general, a relationship between study processingstrategies and academic performance. In particular, the evidence established shows apositive relationship between deep processing strategies and academic performance(Fenollar et al., 2007; Phan, 2008a, 2009; Simons et al., 2004). In contrast, researchstudies exploring the relationship between surface processing strategies and academicperformance have reported inconclusive findings. For example, Phan (2008a), Simonset al. (2004), and Watkins’s (2001) studies support the hypothesised negative relationbetween surface processing strategies and academic performance, whereas otherstudies show no significant relationship between the two constructs (Dupeyrat &Mariné, 2005; Fenollar et al., 2007; Phan, 2007).

The study of reflective thinking practice derives from the works of Dewey (1909/1933) and Schön (1983, 1987). Reflective thinking practice is concerned with theconsequences of ideas and the possibility that future physical actions may be used tosolve a variety of personal and professional problems. In educational psychologyresearch, interest has emerged in the study of reflective thinking practice as an ante-cedent of academic performance (Leung & Kember, 2003; Phan, 2007). In addition,research pertaining to reflective thinking practice has been extended to encompass thetransformative education work of Mezirow (1991, 1998). According to Leung andKember (2003), based on Mezirow’s theoretical ideas, reflective thinking practicemay be categorised into four distinct phases. In order of importance, these are: habit-ual action, understanding, reflection, and critical thinking. Habitual action is amechanical and automatic activity that is performed with little conscious thought.Understanding is learning and reading without relating to other situations. Reflectionconcerns active, persistent, and careful consideration of any assumptions or beliefsgrounded in our consciousness. Finally, critical thinking is considered a higher levelof reflective thinking that involves us becoming more aware of why we perceivethings, the way we feel, the way we act, and what we do.

Empirical research to date has emphasised the importance of the four phases ofreflective thinking practice in teaching and learning (Leung & Kember, 2003;Mezirow, 1991). For example, using confirmatory factor analysis Leung and Kember(2003) found that a surface approach to learning is in line with habitual action,whereas a deep approach to learning is more associated with understanding, reflection,and critical thinking. In a recent study, Phan (2007) used structural equation modellingto show that a surface learning approach is predictive of habitual action, whereas adeep learning approach is predictive of understanding and critical thinking. The fourphases of reflective thinking practice are also reported to make a contribution to theprediction of academic performance; for example, habitual action and understandingare related negatively to academic performance (Phan, 2007, 2008a), whereas reflec-tion and critical thinking positively predict academic performance (Phan, 2008a).

Recently, in the area of motivational research, effort has also featured as an impor-tant construct in determining the success of students’ academic learning (Elliot et al.,1999). Effort refers to the overall amount of effort expended in the process of studying

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(Zimmerman & Risemberg, 1997). Research evidence shows that effort makes aunique contribution to the prediction of academic performance and, more importantly,acts as a potent mediator. Furthermore, the types of goals and study processing strat-egies that students adopt or engage in relate to the amount of effort that is expended(Chouinard, Karsenti, & Roy, 2007; Fenollar et al., 2007). For example, students whofeel that mastering skills and increasing understanding and knowledge is importantengage more in deep processing strategies and believe in effort in the cause of successand/or failure (Elliot et al., 1999; Miller, Greene, Montalvo, Ravindran, & Nichols,1996; Seifert & O’Keefe, 2001).

The importance of achievement goals, study processing strategies, effort, andreflective thinking practice in teaching and learning is reflected in a number of recentstudies. Analysis of the evidence indicates two independent strands of research: inves-tigation of the relations between achievement goals, study processing strategies, effort,and academic performance (e.g., Dupeyrat & Mariné, 2005; Elliot et al., 1999; Fenollaret al., 2007; Phan, 2008a), and investigation of study processing strategies, reflectivethinking practice, and academic performance (e.g., Phan, 2007; Simons et al., 2004;Watkins, 2001). Phan (2008a) presents an alternative research model that amalgamatesthese two lines of inquiry, provides theoretical insight into the relations betweenachievement goals, study processing strategies, effort, and reflective thinking practice,and how these constructs fit into the larger dynamic system of teaching and learning.

The present study: analysis of different theoretical frameworks

The conceptual model, as illustrated in Figure 1, is based on existing research findingspertaining to direct and indirect relations between four theoretical frameworks andacademic performance. This study, in contrast to previous studies (e.g., Dupeyrat &Mariné, 2005; Elliot et al., 1999; Fenollar et al., 2007; Phan, 2008a), attempts toexplore within one conceptual model the interrelatedness of goal orientation, deepprocessing, effort, reflective thinking practice (reflection, critical thinking), andlearning outcome. Based on a synthesis of the literature, the following hypothesesguided this research study:

HP1: There will be direct relationships between goal orientation and deep processingstrategies, effort, reflection, and critical thinking.

HP1a: A mastery goal orientation will exert direct positive effects on deep processingstrategies, effort, reflection, critical thinking, and academic performance(including both academic learning and achievement).

Figure 1. A conceptual model of different theories of learning (Mastery = mastery goals, Per-App = performance approach goals, Deep = deep processing strategies, Critical = critical thinking, Perform-1 = academic learning, Perform-2 = academic achievement). Hypothesised relations are denoted by dotted paths.Hypothesis 1a is formed on the basis of previous research evidence. For example,students who adopt mastery goals are more likely to use deep processing strategies(Dupeyrat & Mariné, 2005; Fenollar et al., 2007; Phan, 2008a, 2009), to expend moreeffort on learning (Chouinard et al., 2007; Elliot et al., 1999; Phan, 2009), and toengage in reflection and critical thinking (Phan, 2008a).

HP1b: A performance-approach goal orientation will exert direct positive effects ondeep processing strategies, effort, reflection, critical thinking, and academicperformance (including both academic learning and achievement).

Likewise, there is evidence to suggest that there is a positive relationship betweena performance-approach goal orientation and effort (Elliot et al., 1999; Fenollar et al.,

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2007; Lopez, 1999; Phan, 2008a) and academic performance (Elliot & Church, 1997;Elliot & McGregor, 1999; Elliot et al., 1999). However, much less is known about therelationship between a performance-goal orientation and reflective thinking practice.Phan (2008a) has found, for instance, that performance-approach goal orientationexerted a small indirect effect on reflection, but not critical thinking. From this incon-clusive evidence, we also explore the relationship between performance-approachgoals and reflection and critical thinking.

[HP2]: There will be direct relationships between effort and deep processing, reflec-tion, critical thinking, and academic performance.

There is evidence that effort exerts direct positive effects on deep processing (Phan,2008a), reflection and critical thinking (Phan, 2008a), and academic performance(Dupeyrat & Mariné, 2005; Elliot et al., 1999).

The importance of effort as an antecedent of academic performance is highlightedin a number of research studies (Dupeyrat & Mariné, 2005; Elliot et al., 1999; Fenollaret al., 2007). Previous research has shown effort to be unrelated to deep processingstrategies (DeBacker & Crowson, 2006; Dupeyrat & Mariné, 2005; Fenollar et al.,2007). Evidence for the effect of effort on reflection is inconclusive and requiresfurther research investigation. In a recent path analysis study, Phan (2009) reported noassociation between the two constructs, whereas Phan (2008a) found that effortexerted a positive influence on students’ reflection.

HP3: There will be direct relationships between academic performance and deepprocessing, reflection, and critical thinking.

HP3a: Deep processing will exert direct positive effects on reflection, critical think-ing, and academic performance.

HP3b: Reflection will exert direct positive effects on critical thinking and academicperformance.

HP3c: Critical thinking will exert a direct positive effect on academic performance.

The hypotheses concerning relationships between deep processing, reflection, crit-ical thinking, and academic performance are consonant with previous research studies.There is clear and consistent evidence to support the hypothesised effect of deepprocessing on academic performance (Fenollar et al., 2007; Phan, 2008a, 2009; Simonset al., 2004). However, much less is known about the relationships between deepprocessing and reflection and critical thinking; for example, Phan (2007, 2008a) foundno significant relationship between the three constructs. There is limited evidence, incontrast, to support the hypothesised relationship between reflection and academicperformance, and critical thinking and academic performance (Phan, 2008a).

HP4: There will be indirect effects of goal orientations, deep processing, effort, andreflection on academic performance.

HP4a: A mastery goal orientation will exert a positive effect on academic perfor-mance indirectly via deep processing strategies, effort, reflection, and criticalthinking.

HP4b: A performance-approach goal orientation will exert a positive effect onacademic performance indirectly via effort, reflection, and critical thinking.

HP4c: Effort will exert a positive effect on academic performance indirectly via deepprocessing strategies, reflection, and critical thinking.

HP4d: Deep processing will exert a positive effect on academic performanceindirectly via reflection and critical thinking.

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HP4e: Reflection will exert a positive effect on academic performance indirectly viacritical thinking.

Existing research studies indicate indirect effects of mastery and performance-approach goals, deep processing, and effort on academic performance. In particular,works previously cited that investigated mastery and performance-approach goals,effort (e.g., Dupeyrat & Mariné, 2005; Elliot et al., 1999; Fenollar et al., 2007), deepprocessing (e.g., Cano, 2005; Fenollar et al., 2007; Simons et al., 2004), and reflectionand critical thinking (e.g., Phan, 2007, 2008a) support the indirect effects of theseconstructs on students’ academic performance.

In summary, the hypotheses outlined differ from previous research (e.g., Dupeyrat& Mariné, 2005; Elliot et al., 1999; Fenollar et al., 2007; Phan, 2008a) on two mainpremises. First, we incorporated two main goal orientations – mastery and perfor-mance-approach – in our conceptual model as these two goal types have been foundto relate positively to deep processing strategies, effort, reflective thinking practice,and academic performance. Second, we limited our research focus to deep processingstrategies and two of the four phases of reflective thinking practice (reflection andcritical thinking) only as these three constructs have been found to exert positiveeffects on students’ academic performance. In contrast, as mentioned previously,surface processing strategies, habitual action, and understanding have been found toexert negative effects on academic performance.

In contrast to previous studies (e.g., Cano, 2005; Dupeyrat & Mariné, 2005; Elliotet al., 1999; Fenollar et al., 2007; Phan, 2008a; Shih, 2005), this study examines theorigin and formation of reflective practice and the success of academic learning.Furthermore, differing from previous research studies that have used academicachievement as an index of students’ performance, this study utilises two indexes:academic learning and academic achievement. Academic learning is concerned withongoing classroom learning and experience; for example, students in this study were

Mastery

Deep

Effort

Critical

Perform-1

Perform-2

Reflection

Per-App

Figure 1. A conceptual model of different theories of learning (Mastery = mastery goals,Per-App = performance approach goals, Deep = deep processing strategies, Critical = criticalthinking, Perform-1 = academic learning, Perform-2 = academic achievement). Hypothesisedrelations are denoted by dotted paths.

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encouraged to work in groups on tutorial questions and to engage in classroomdiscussions and participation. Academic achievement, in contrast, involved individualwork and was examined using both tests and the final end-of-semester examination.

Method

Participants

The sample consisted of 347 second- and third-year (151 women, 196 men) studentsenrolled in an educational psychology course at a local university. The ages range was19–54 (M = 23, SD = 1.98). Educational psychology is a compulsory subject whichall education students must take. It can also be taken as an elective by students whoare not majoring in education.

The instruments were administered in tutorial classes with the assistance of a tutor.Participation by the students was voluntary and no remuneration was provided.Students were instructed to write down their student number for the purpose ofcollecting their overall marks in educational psychology. Students were assured ofanonymity and were informed why their overall performance marks in educationalpsychology were needed.

Measures

Participants completed a questionnaire developed to assess mastery and performance-approach goals, deep processing strategies, effort, reflection, and critical thinking. Thequestions were answered on a seven-point Likert-type rating scale (1 = ‘stronglydisagree’ to 7 = ‘strongly agree’). Mastery goals (e.g., ‘An important reason why I domy academic work is because I like to learn new things’) and performance-approachgoals (e.g., ‘It’s important to me that the other students in my tutorial classes think thatI am good at my work’) were measured using Midgley et al.’s (1998) five-item scales.Deep processing (e.g., ‘I study the course material by underlying the most importantpart’) was measured using five items from Simons et al.’s (2004) instrument. Effort(e.g., ‘I put a lot effort into preparing for the exam’) was measured using three itemsfrom Elliot et al.’s (1999) instrument. Reflection and critical thinking were measuredusing the Kember et al. (2000) Reflective Thinking Questionnaire (RTQ). The sampleitems include ‘I often re-appraise my experience, so I can learn from it and improvefor my next performance’ (reflection), and ‘This course has challenged some of myfirmly held ideas’ (critical thinking). Sample items and Cronbach’s alpha values forthe scales are presented in Table 1.

Finally, academic performance was measured by two indexes: academic achieve-ment consisting of a mini-test (contributing 20% to the total academic performancescore) and a final exam (50%); and academic learning consisting of a written assign-ment (15%) and ongoing class participation and attendance (15%) and ongoing classparticipation and attendance in tutorial classes. In general, questions in the mini-testand final exam are structured to reflect more than just recall of factual information.For example: ‘How has your culture shaped the way you think about the concepts offormal and informal learning?’ and ‘From your classroom experience, give clearexamples to show how both interpsychological and intrapsychological processes takeplace.’ Similarly, tutorials are based on group and classroom discussion of questionsthat require much more than just reading and recall of information. For example:

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‘Constructivists believe that cognitive processes like perception and memory involveinterpretation. Do you agree? In what ways are interpretative processes involved inperception and memory?’ and ‘Should classroom learning in the South Pacific bebased on metacognitive principles? How might such classrooms operate? Whatmight be the role of the teacher in a metacognitively oriented classroom?’

Statistical analysis

The conceptual model illustrated in Figure 1 was tested and analysed using struc-tural equation modelling (SEM) procedures. Descriptive statistics were calculatedand preliminary analyses were carried out using SPSS 15. SEM is considered anappropriate statistical procedure as it enables examination of both direct and mediat-ing effects between motivational variables and academic performance (Bollen,1989; Byrne, 1998; Kline, 2005). Furthermore, SEM techniques have a strong theo-retical grounding and empirical support, and allow the testing of competing modelsand/or refinement of an a priori model to fit the data. SEM as a statistical procedureis advantageous as it permits the researcher to test and evaluate competing a priorimodels simultaneously with the overall model fit being provided. The mostcommon fit indexes that are recommended when reporting SEM analyses include:the chi-square statistic; the Steiger-Lind root mean square error of approximation(RMSEA; Steiger, 1990) with its 90% confidence interval; the Bentler comparativefit index (CFI; Bentler, 1990); and the non-normed fit index (NNFI; Bentler &Bonett, 1980).

We used LISREL-8.72 with covariance matrices and maximum likelihood (ML)procedures to test the structural equations. The statistical program LISREL-8.72 forthe PC (similar to SPSS AMOS 7), developed by Jöreskog and Sörbom (2001),

Table 1. A summary of items and their reliabilities.

M SD

Scale Sample items M W M W n α

Mastery goals An important reason why I do my school work is because I like to learn new things

5.87 5.80 1.38 1.34 5 .89

Performance-approach goals

It’s important to me that the other students in my classes think that I am good at my work

4.84 4.92 1.35 1.40 5 .88

Deep processing I study the course material by underlying the most important part

5.08 4.94 1.31 1.23 5 .84

Effort I put a lot of effort into preparing for the exam

5.83 5.71 1.13 1.10 3 .77

Reflection I often re-appraise my experience, so I can learn from it and improve for my next performance

5.82 5.66 1.48 1.46 4 .80

Critical thinking This course has challenged some of my firmly held ideas

5.28 5.58 1.95 1.76 4 .94

Note: For means (M) and standard deviations (SD), values are placed in two columns – men (M) andwomen (W).

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Educational Psychology 305

enables the testing of a priori models and provides various goodness-of-fit indexvalues. We analysed covariance matrices because correlation matrix analysis is knownto involve potential problems, such as producing incorrect goodness-of-fit measuresand standard errors (Byrne, 1998; Jöreskog & Sörbom, 2001). Furthermore, the MLprocedure was chosen as it has been shown to perform reasonably well with multivari-ate normally distributed data (Chou & Bentler, 1995). A number of goodness of fitindex values are calculated by LISREL; however, the three reported in this study arethe CFI, the NNFI, and the RMSEA. Models with CFI and NNFI values close to .95and RMSEA values below .05 are normally considered an indication of good modelfit (Byrne, 1998).

Results

The means, standard deviations, and Cronbach’s alpha values for the sample arepresented in Table 1. The alpha values ranged from .77 to .94 for the six subscales,and are comparable with previous findings. Kurtosis values for the measured indica-tors ranged from −.68 to 2.15, and the skewness values ranged from −1.75 to −.50;these values indicate a normal distribution of the data (Kline, 2005). The correlationmatrix of eta and ksi is presented in Table 2.

In the initial analysis, the a priori model (Model M1) showed a relatively poor fitto the data (CFI = .82, NNFI = .80, RMSEA = .09). The modification fit index (MI)recommended by the LISREL program indicated correlated errors between two itemsdescriptive of the mastery goals scale, and four items descriptive of the deep process-ing strategies scale. It is not uncommon to find correlated measurement errorsbetween items descriptive of a particular scale (Byrne, 1998). On this basis, themodel was amended and the a posteriori model (Model M2) involved the freeing ofthree additional paths. The respecified model showed a good fit, as reflected by thevarious goodness-of-fit index values (CFI = .93, NNFI = .92, RMSEA = .06). Theaddition of the three paths resulted in a statistically significant difference in fitbetween the two models (∆χ2

(M1-M3)[3,n = 347] = 770.83, p < .001).1 Figure 2shows the respecified model with paths from the measured indicators to their latentvariables statistically significant at the .05 and .01 levels. For example, the factorloadings were .68–.91 for items descriptive of the mastery goals scale, .49–.95 foritems descriptive of the performance-approach goals scale, .44–.91 for items descrip-tive of the deep processing strategies scale, .44–.89 for items descriptive of the effort

Table 2. Correlation matrix of eta and ksi.

Deep Effort Reflect Critical Perf-1 Perf-2 Mastery Per-App

Deep 1.00Effort .29* 1.00Reflect .07 −.06 1.00Critical .13 .04 .03 1.00Perf-1 .18 .10 .22* .71* 1.00Perf-2 .07 .08 .34* .57* .49* 1.00Mastery .24* .01 .69* .08 .22* .31* 1.00Per-App .08 .19 .29* .06 −.02 −.02 .12* 1.00

Note: * p < .05. Mastery = mastery goals, Per-App = performance approach goals, Deep = deep processingstrategies, Critical = critical thinking, Perform-1 = academic learning, Perform-2 = academic achievement.

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scale, .78–.83 for items descriptive of the reflection scale, and .75–.93 for itemsdescriptive of the critical thinking scale. The structural coefficients for statisticallysignificant paths between the latent factors ranged from −.19 to .70.Figure 2. A full model of different theories of learning (Mastery = mastery goals, Per-App = performance approach goals, Deep = deep processing strategies, Critical = critical thinking, Perform-1 = academic learning, Perform-2 = academic achievement). Non-significant paths have been omitted for clarity; the paths included are significant at p < .05.Table 3 displays direct and mediating effects. There were two direct effects onacademic achievement (critical thinking: β = .57; reflection: β = .31), and three directeffects on academic learning (critical thinking: β = .70; reflection: β = .26; perfor-mance-approach goals: β = −.16). The strongest effect on both academic achievementand academic learning was provided by critical thinking (β = .57 and β = .70, respec-tively), followed by mastery goals (β = .30) and reflection (β = .27) for academicachievement. The strongest effect on reflection was provided by mastery goals (β =.67), followed by performance-approach goals (β = .21) and effort (β = −.11).Likewise, mastery goals (β = .69) and performance-approach goals (β = .23) directlyinfluenced reflection, and effort (β = .29) and mastery goals (β = .24) directly influ-enced deep processing strategies. The strongest effect on deep processing strategieswas provided by effort (β = .29), followed by mastery goals (β = .24) and performance-approach goals (β = .06). Only performance-approach goals exerted a direct effect oneffort (β = .19). Finally, mastery goals and performance-approach goals influencedacademic performance (β = .24 and β = .11, respectively) and academic learning (β =.13 and β = .11, respectively) indirectly and, similarly, performance-approach goalsexerted a relatively small indirect effect on deep processing strategies (β = .06).

To extend the analysis, a one-way multivariate analysis test was performed to explorethe possibility of gender differences in relation to the variables under investigation.Previous research studies have reported inconclusive findings in relation to gender and

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Figure 2. A full model of different theories of learning (Mastery = mastery goals, Per-App =performance approach goals, Deep = deep processing strategies, Critical = critical thinking,Perform-1 = academic learning, Perform-2 = academic achievement). Non-significant pathshave been omitted for clarity; the paths included are significant at p < .05.

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Educational Psychology 307

study processing strategies and reflective thinking practice. For example, in the areaof reflective thinking practice, Phan (2007) did not find statistically significant differ-ences between men and women in the four phases of reflection (Mezirow, 1991, 1998).In contrast, in another study involving students enrolled in a human development course,Phan (2008a) found that women were more engaged in understanding than men. In stud-ies of processing strategies, Cano (2005) and Smith and Miller (2005) found that menand women differed in the study strategies they used.

In the one-way MANOVA,2 six dependent variables were used: mastery goals,performance-approach goals, deep processing strategies, effort, reflection, and critical

Table 3. Indirect, direct, and total effects.

Effect Direct Indirect Total

On academic learning (Perform-1):of critical thinking .70** – .70**of reflection .26* −.05 .21of deep processing .06 .06 .12of effort .10 .01 .11of mastery goals −.02 .24** .22**of performance approach goals −.16* .11** −.05

On academic achievement (Perform-2):of critical thinking .57** – .57**of reflection .31** −.04 .27**of deep processing −.06 .04 −.02of effort .11 −.03 .08of mastery goals .07 .23** .30**of performance approach goals −.07 .11** .04

On critical thinking:of reflection −.07 – −.07of deep processing .11 .01 .12of effort −.01 .04 .03of mastery goals .10 −.02 .08of performance approach goals .06 −.01 .05

On reflection:of deep processing −.09 – −.09of effort −.08 −.03 −.11*of mastery goals .69** −.02 .67**of performance approach goals .23** −.02 .21**

On deep processing:of effort .29** – .29**of mastery goals .24** .00 .24**of performance approach goals .06** .06**

On effort:of mastery goals −.02 – −.02of performance approach goals .19** – .19**

Note: * p < .05; ** p < .01.

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308 H.P. Phan

thinking. The independent variable was gender. To avoid Type I error, a Bonferronicorrection of p < .008 was applied. Levene’s tests showed that the assumption of equalvariance of all variables had been met (p > .05). The results indicated no statisticallysignificant difference between men and women on the combined dependent variables(F[6,340] = 1.23, Wilk’s λ = .98; η2 = .02). When the results for the dependentvariables were considered separately using adjusted alpha values, no statisticalsignificance was observed.

Discussion

This study involved an examination of four different theoretical frameworks withinone conceptual model. In particular, causal modelling procedures were used to explorethe interrelations between goal orientations, deep processing, effort, reflective think-ing, and academic performance. In general, the evidence supports the hypothesisedmodel in Figure 1. Furthermore, the findings lend support to current research lookingat the unification and inclusion of different theories of learning (Fenollar et al., 2007;Phan, 2008a). Below, results are discussed with reference to the final structural model,as illustrated in Figure 2.

Direct relationships: reflection, critical thinking, and goal orientations as antecedents of academic performance

This study shows that academic learning is influenced by reflection, critical thinking,and performance-approach goals. Academic achievement, similarly, is influenced byreflection and critical thinking.

In contrast to previous studies (Elliot & Church, 1997; Elliot & McGregor, 1999;Elliot et al., 1999) which have found a positive relationship between performance-approach goals and academic performance, the finding in this study was of a negativeeffect between the two constructs instead. A performance-approach goal orientation,in this sample, was likely to result in a decrease in academic learning. As students withperformance-approach goals value positive evaluations of their ability (Miller et al.,1996; Shih, 2005), it is not surprising to find that this goal orientation leads to adecline in academic learning. In essence, performance-approach goals reflectstudents’ desire to prove to others their academic competence, and this isaccomplished by succeeding in tests and/or the final exam and not, in contrast, by theacquisition of skills in classroom learning.

The positive effects of both reflection and critical thinking on academic learningand achievement in this study support existing research evidence (Phan, 2008a).Reflection and critical thinking encourage the cultivation of meaningful learning, thedevelopment of skills such as articulation, and the theorisation of new knowledge.Students who see learning as having the initiative and capability to reflect and togenerate new theoretical knowledge are more likely to succeed academically.

Direct relationships: effort and mastery goal orientation as antecedents ofdeep processing strategies

Consistent with existing research (DeBacker & Crowson, 2006; Fenollar et al., 2007;Phan, 2008a, 2009; Simons et al., 2004), the findings show that deep processing wasinfluenced by both mastery goals and effort. Students interested in the ability to

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Educational Psychology 309

acquire knowledge and to develop new skills were more inclined to engage in deeplearning. This deep learning also involves the notion of being able to relate knowledgeback to prior knowledge and experience. Effort expenditure was also found to lead todeep processing. Students who view effort expenditure as paramount to learningseem likely to align closely to the use of deep processing to enhance their academicperformance. One could say, then, that effort is analogous to engagement in deepprocessing strategies. Effort involves hard work and time: both of these serve as cata-lysts to help students in their processing of knowledge. This line of reasoning isconsistent with previous research (Phan, 2008a) showing a positive effect of effort ondeep processing. This significant relationship was not found in other studies(DeBacker & Crowson, 2006; Dupeyrat & Mariné, 2005; Fenollar et al., 2007), whichinstead indicated the non-relatedness of the two constructs.

Direct relationships: goal orientations as antecedents of reflection

The evidence supports the hypothesis of a direct relationship between goal orienta-tions (both mastery and performance-approach) and reflection. Reflection, in thiscase, was affected positively by both mastery and performance-approach goals. Thisfinding is pivotal as it provides a theoretical contribution to our understanding ofreflection (Phan, 2008a). Students who pursue performance-approach goals are, ingeneral, more likely to adopt the use of reflection, as this process may facilitate betterunderstanding and analysis of knowledge and skill improvement. Likewise, the notionof learning to master new skills and knowledge for interest and skill development –that is, mastery goals – may also help students to practice the art of reflection. Reflec-tion, in turn, may enable students to articulate their thoughts, current knowledge, andexperience which, ultimately, leads to academic achievement and learning.

Gender differences

Male and female students did not differ in their goal orientations, deep processingstrategies, effort expenditure, or reflective thinking practice. This finding, especiallypertaining to reflective thinking practice and deep processing strategies, is consistentwith research conducted by Phan (2007). Also, Phan (2008a) reported no statisticallysignificant gender differences in performance goal orientation, effort, study process-ing strategies, or reflective thinking practice. One possible explanation for this is thatschools’ institutional cultures may affect men and women equally in their academiclearning. There is also an argument that the ethos of a collective group or society mayalso contribute to individuals’ determination and motivation to excel academically.Generalised assumptions and beliefs about knowledge and knowing may help tocreate a specific mindset. In essence, values and beliefs shared by the people of aparticular society could subsume other factors (e.g., gender, ethnicity) in determiningteaching and learning processes.

Indirect effects: goal orientation

Table 3 shows that both mastery and performance-approach goals exerted indirecteffects on academic learning and achievement. Furthermore, performance-approachgoals also exerted a small indirect effect on deep processing strategies. These findingsreinforce the potency of achievement goals in contributing to students’ academic

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310 H.P. Phan

performance and learning strategies. The direct and indirect positive effects ofmastery and performance-approach goals provide a premise for future research toinvestigate the mediating mechanisms of effort, deep processing strategies, and reflec-tive thinking practice.

Conclusion

In conclusion, the conceptual model and empirical findings in this study make theo-retical and practical contributions to the literature on achievement goals, effort, deepprocessing, and reflective thinking practice. This investigation is significant as itinvolved different theoretical orientations within one framework. Theoretically speak-ing, the evidence obtained emphasises mastery and performance-approach goals,reflection, and critical thinking as determinants of students’ learning and academicachievement. Furthermore, the theoretical contention that mastery and performance-approach goals contribute to the prediction of deep processing strategies (Dupeyrat &Mariné, 2005; Fenollar et al., 2007; Phan, 2008a, 2009) and effort (Chouinard et al.,2007; Elliot et al., 1999; Phan, 2009) was supported. In terms of the differentmotivational variables under investigation, reflective thinking practice is the mostunder-investigated area of inquiry in educational psychology research. Preliminaryresearch studies (Leung & Kember, 2003; Phan, 2007, 2008b) in this area haveacknowledged the importance of this practice in facilitating academic performance.This study’s findings provide additional understanding of how reflective thinkingpractice features in a dynamic ongoing system that enhances academic success.

Although this research study has established some important and useful findings,there are however some limitations in methodology and research design that warrantfurther research investigation.

First, this study, although making a unique theoretical contribution to the litera-ture, has some inconclusive findings when compared with previous research. Forexample, research studies to date have shown that the four phases of reflective think-ing practice (Leung & Kember, 2003; Mezirow, 1991, 1998) are related in a unidirec-tional manner – habitual action predicts understanding and understanding predictsreflection (Phan, 2007, 2008a). In this study, the data and subsequent SEM analysesfailed to repeat this pattern of evidence. Similarly, in contrast to previous researchstudies (Fenollar et al., 2007; Phan, 2008a; Simons et al., 2004), this investigation didnot find a positive relationship between deep processing strategies and academicperformance. A note of caution: SEM procedures may in many cases be ‘quasi’ or‘exploratory’ in their analyses (Byrne, 1998). Often, different a priori and a posteriorimodels are tested, evaluated, and compared with each other to determine which modelfits the data best. More stringent tests of causation should be conducted for confirma-tion of structural relations.

Second, the correlational data and research design used in this study precluded anyinferring of causality between mastery and performance-approach goals, effort, deepprocessing strategies, reflection, and critical thinking and academic performance.Methodologically, the use of longitudinal data in conjunction with latent variablemethods may serve as a stronger basis for determining cause-and-effect relationshipsbetween, for example, study processing strategies and reflective thinking practice overtime. In a recent two-wave panel study, Phan (2008b) used longitudinal data toexplore causality and reciprocality between students’ approaches to their learning andepistemological beliefs (SAL) and epistemological beliefs. Likewise, motivational

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Educational Psychology 311

research in the area of self-concept has also used multiple time points of data collec-tion to infer causality between self-concept and academic performance (Marsh, 1990;Marsh & Yeung, 1997). Longitudinal data may enable us to explore the direction ofcause-and-effect relationships between study processing strategies and reflectivethinking practice. For instance, one might argue that reflective thinking, instead ofbeing a product of study processing strategies, is in fact over time a source of studyprocessing strategies. Likewise, one could postulate that rather than a product of studyprocessing strategies and reflective thinking, academic learning could actually influ-ence the types of strategies that students adopt and the reflective thought that theydevelop over time. Furthermore, the sample used in this study was average in size.Future research could use bigger samples, especially when causal modelling proce-dures such as SEM are used.

Third, further research could examine how particular study strategies and reflectivethinking fit in with the learning environment. Studies could explore how the classroomenvironment influences students’ alignment to a specific learning strategy and theirsubsequent academic success. For example, Wong and Watkins (1998) found in theirstudy of Chinese students that an enjoyable classroom environment is important as itmediates relationships between a deep learning approach and high-level achievement.

Fourth, quantitative methods alone cannot explain patterns of goal orientation,study processing strategies, or reflective thinking practice. Students’ reflective think-ing practice cannot be captured solely from correlational and questionnaire data. Qual-itative methods (e.g., ethnography) may allow us to explore in-depth the socioculturaland contextual nature of reflective thinking: for example, does an individual’s reflec-tive thinking practice and existing knowledge and experience depend, in part, onsocial settings or context?

Notes1. Based on Byrne’s (1998) recommendation, paths were added/deleted and calculated one at

a time. For example: Model(M1) – Model(M2), Model(M3) – Model(M2).2. Preliminary assumption analysis was conducted to ensure for normality, linearity, and

univariate and multivariate outliers, and no violations were noted.

ReferencesAmes, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strate-

gies and motivation processes. Journal of Educational Psychology, 80, 260–267.Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin,

107, 238–246.Bentler, P.M., & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis of

covariance structures. Psychological Bulletin, 88, 588–606.Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley.Byrne, B.M. (1998). Structural equation modelling with LISREL, PRELIS, and SIMPLIS: Basic

concepts, applications, and programming. Mahwah, NJ: Erlbaum.Cano, F. (2005). Epistemological beliefs and approaches to learning: Their change through

secondary school and their influence on academic performance. British Journal ofEducational Psychology, 75, 203–221.

Chou, C.P., & Bentler, P.M. (1995). Estimates and tests in structural equation modelling. InR.H. Hoyle (Ed.), Structural equation modelling: Concepts, issues, and applications,(pp. 37–55). Thousand Oaks, CA: Sage.

Chouinard, R., Karsenti, T., & Roy, N. (2007). Relations among competence beliefs, utilityvalue, achievement goals, and effort in mathematics. British Journal of EducationalPsychology, 77, 501–517.

Dow

nloa

ded

by [

Uni

vers

ity o

f M

isso

uri C

olum

bia]

at 1

9:43

10

Sept

embe

r 20

14

312 H.P. Phan

DeBacker, T.K., & Crowson, H.M. (2006). Influences on cognitive engagement:Epistemological beliefs and need for closure. British Journal of Educational Psychology, 76,535–551.

Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to theeducative process. Boston: D.C. Health. (Original work published 1909).

Dupeyrat, C., & Mariné, C. (2005). Implicit theories of intelligence, goal orientation,cognitive engagement, and achievement: A test of Dweck’s model with returning toschool adults. Contemporary Educational Psychology, 30, 43–59.

Elliot, A.J. (1997). Integrating the ‘classic’ and ‘contemporary’ approaches to achievementmotivation: A hierarchical model of approach and avoidance motivation. In M.L. Maher& P.R. Pintrich (Eds.), Advances in motivation and achievement (Vol. 10, pp. 243–279).Greenwich, CT: JAI Press.

Elliot, A.J. (1999). Approach and avoidance motivation and achievement goals. EducationalPsychologist, 34, 169–189.

Elliot, A.J., & Church, M.A. (1997). A hierarchical model of approach and avoidanceachievement motivation. Journal of Personality and Social Psychology, 72, 218–232.

Elliot, A.J., & Harackiewicz, J.M. (1996). Approach and avoidance achievement goals andintrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology,70, 968–980.

Elliot, A.J., & McGregor, H.A. (1999). Test anxiety and the hierarchical model of approachand avoidance achievement motivation. Journal of Personality and Social Psychology, 76,628–644.

Elliot, A.J., McGregor, H.A., & Gable, S. (1999). Achievement goals, study strategies,and exam performances: A mediational analysis. Journal of Educational Psychology, 91,549–563.

Elliot, E.S., & Dweck, C.S. (1988). Goals: An approach to motivation and achievement.Journal of Personality and Social Psychology, 54, 5–12.

Entwistle, N.J. (1997). Reconstructing approaches to learning: A response to Webb. HigherEducation, 33, 213–218.

Entwistle, N.J., & Tait, H. (1994). The Revised Approaches to Study Inventory. Edinburgh, UK:Edinburgh University Centre for Research into Learning and Instruction.

Fenollar, P., Román, S., & Cuestas, P.J. (2007). University students’ academic performance:An integrative conceptual framework and empirical analysis. British Journal of EducationalPsychology, 77, 873–891.

Fernsten, L., & Fernsten, J. (2005). Portfolio assessment and reflection: Enhancing learningthrough effective practice. Reflective Practice, 6, 303–309.

Jöreskog, K., & Sörbom, D. (2001). LISREL 8: User’s reference guide [software manual].Chicago: Scientific Software.

Kember, D., Leung, D., Jones, A., Loke, A.Y., McKay, J., Sinclair, K., et al. (2000). Develop-ment of a questionnaire to measure the level of reflective thinking. Assessment and Evalua-tion in Higher Education, 25, 381–389.

Kline, R.B. (2005). Principles and practice of structural equation modelling (2nd ed.). NewYork: Guildford.

Leung, D.Y.P., & Kember, D. (2003). The relationship between approaches to learning andreflection upon practice. Educational Psychology, 23, 61–71.

Lopez, D.E. (1999). Social cognitive influences on self-regulated learning: The impact ofaction-control beliefs and academic goals on achievement-related outcomes. Learning andIndividual Differences, 11, 301–319.

Marsh, H.W. (1990). Causal ordering of academic self-concept and academic achievement: Amultiwave, longitudinal panel analysis. Journal of Educational Psychology, 82, 646–656.

Marsh, H.W., & Yeung, A.S. (1997). Causal effects of academic self-concept on academicachievement: Structural equation models of longitudinal data. Journal of EducationalPsychology, 89, 41–54.

Marton, F., & Säljö, R. (1976). On qualitative differences in learning – I: Outcome andprocess. British Journal of Educational Psychology, 46, 4–11.

Meece, J.L., Blumenfeld, P.C., & Hoyle, R. (1988). Students’ goal orientations andcognitive engagement in classroom activities. Journal of Educational Psychology, 80,514–523.

Dow

nloa

ded

by [

Uni

vers

ity o

f M

isso

uri C

olum

bia]

at 1

9:43

10

Sept

embe

r 20

14

Educational Psychology 313

Mezirow, J. (1991). Transformative dimensions of adult learning. San Francisco: Jossey-Bass.Mezirow, J. (1998). On critical reflection. Adult Education Quarterly, 48, 185–198.Midgley, C., Kaplan, A., Middleton, M., Maehr, M.L., Urdan, T., Anderman, L.H., et al.

(1998). The development and validation of scales assessing students’ achievement goalorientations. Contemporary Educational Psychology, 23, 113–131.

Miller, R.B., Greene, B.A., Montalvo, G.P., Ravindran, B., & Nichols, J.D. (1996). Engage-ment in academic work: The role of learning goals, future consequences, pleasing others,and perceived ability. Contemporary Educational Psychology, 21, 388–422.

Nolen, S.B., & Haladyns, T.M. (1990). Personal and environmental influences on students’beliefs about effective study strategies. Contemporary Educational Psychology, 15, 116–130.

Phan, H.P. (2007). Examination of student learning approaches, reflective thinking, and self-efficacy beliefs at the University of the South Pacific: A path analysis approach.Educational Psychology, 27, 789–806.

Phan, H.P. (2008a). Unifying different theories of learning: Theoretical framework andempirical evidence. Educational Psychology, 28, 325–340.

Phan, H.P. (2008b). Predicting change in epistemological beliefs, reflective thinking, andlearning approaches: A longitudinal study. British Journal of Educational Psychology, 78,75–93.

Phan, H.P. (2009). Amalgamation of future time orientation, epistemological beliefs,achievement goals and study strategies: Empirical evidence established. British Journal ofEducational Psychology, 79, 155–173.

Schön, D. (1983). The reflective practitioner: How professionals think in action. New York:Basic Books.

Schön, D. (1987). Educating the reflective practitioner. San Francisco: Jossey-Bass.Seifert, T.L., & O’Keefe, B.A. (2001). The relationship of work avoidance and learning goals

to perceived competence, externality and meaning. British Journal of Educational Psychol-ogy, 71, 81–92.

Shih, S.-S. (2005). Taiwanese sixth graders’ achievement goals and their motivation, strategyuse, and grades: An examination of the multiple goal perspective. The Elementary SchoolJournal, 106, 39–58.

Simons, J., Dewitte, S., & Lens, W. (2004). The role of different types of instrumentality inmotivation, study strategies, and performance: Know why you learn, so you’ll know whatyou learn. British Journal of Educational Psychology, 74, 343–360.

Smith, N., & Miller, R.J. (2005). Learning approaches: Examination type, discipline of study,and gender. Educational Psychology, 25, 43–53.

Steiger, J.H. (1990). Structural model evaluation and modification: An interval estimationapproach. Multivariate Behavioral Research, 25, 173–180.

Stipek, D.J., & Kowalski, P.S. (1989). Learned helplessness in task-orienting versus perfor-mance-orienting test conditions. Journal of Educational Psychology, 81, 384–391.

Tait, H., & Entwistle, N.J. (1996). Identifying students at risk through ineffective study strate-gies. Higher Education, 31, 97–116.

Valle, A., Cabanach, R.G., & Núnez, J.C. (2003). Multiple goals, motivation and academiclearning. British Journal of Educational Psychology, 73, 71–87.

Watkins, D. (2001). Correlates of approaches to learning: A cross-cultural meta-analysis. InR. Sternberg & L. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles(pp. 165–195). New Jersey: Lawrence Erlbaum.

Wong, N., & Watkins, D. (1998). A longitudinal study of psychosocial environment andlearning approaches in the Hong Kong classroom. Journal of Educational Research, 91,247–254.

Zimmerman, B.J., & Risemberg, R. (1997). Self-regulatory dimensions of academic learningand motivation. In G. Phye (Ed.), Handbook of academic learning (pp. 105–125). NewYork: Academic Press.

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