Implicit theories, epistemic beliefs, and science motivation: A person-centered approach

Chen, Jason

doi:10.1016/j.lindif.2012.07.013

Cited by 94 publications

(134 citation statements)

References 80 publications

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“…This study sought to better understand how students approach foundational courses by identifying learning profiles of students who share common motivational and self‐regulated learning characteristics; the students were taking courses in a suite of foundational computer science courses designed for both computer science majors and non‐computer science majors, some of whom were engineering students. Our identification of five profiles replicates and extends findings from prior studies of college students in nonengineering (Chen, ; Daniels et al, ; Schwinger et al, ; Shell & Husman, ) and engineering or technical fields (Entwistle & McCune, ; Shell & Soh, ). The profiles also replicate profiles found in K‐12 settings (Chen, ; Hayenga & Corpus, ; Tuominen‐Soini et al, ; Vansteenkiste, Sierens, Soenens, Luyckx, & Lens, ).…”

Section: Discussionsupporting

confidence: 86%

“…Our research sought to determine what motivational and self‐regulated learning profiles engineering students adopt in foundational courses. To identify learning profiles, we took the learner‐centered approach that has been utilized in most recent profile studies (Chen, ; Conley, ; Daniels et al, ; Schwinger et al, ; Shell & Soh, ; Tuominen‐Soini et al, ). The learner‐centered approach determines if groups of students can be identified who share common motivational and self‐regulated learning characteristics.…”

Section: Research Aimsmentioning

confidence: 99%

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Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Nelson

Shell

Husman

et al. 2014

J of Engineering Edu

136

123

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Background Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self‐regulation constructs. Purpose (Hypothesis) This project sought to determine what motivational and self‐regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self‐regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed. Design/Method Five hundred and thirty‐eight students, 332 of them engineering majors, were surveyed on motivation and self‐regulation variables. Data were analyzed from a learner‐centered profile approach using cluster analysis. Results We obtained a five‐cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering‐tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not. Conclusions Findings indicate the motivational and self‐regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self‐regulated learning scaffolds in the classroom.

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Section: Discussionsupporting

confidence: 86%

Section: Research Aimsmentioning

confidence: 99%

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Nelson

Shell

Husman

et al. 2014

J of Engineering Edu

136

123

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“…Masteryapproach goals have also been found to have a weak relationship with academic achievement, with some studies showing positive relationships with performance (e.g., Bipp, Steinmayr, & Spinath, 2012;Bong, 2001;Chen, 2012;Chiang, Yeh, Lin, & Hwang, 2011) and others reporting no relationship (e.g., Corkin, Yu, & Lindt, 2011;Elliot & Murayama, 2008;Harackiewicz, Durik, Barron, Linnenbrink-Garcia, & Tauer, 2008;Hulleman, Durik, Schweigert, & Harackiewicz, 2008). Although the relationship between mastery-approach goals and performance in work domains (r ϭ .27) is typically stronger than in athletic or educational settings (r ϭ .17 and r ϭ .13 respectively, see Yperen et al, 2014), the overall magnitude of this rela-tionship is typically weak in achievement domains.…”

Section: Achievement Goal Orientationsmentioning

confidence: 99%

Achievement goals, emotions, learning, and performance: A process model.

2015

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The present study evaluates the relations between achievement goals, emotions, learning strategies, and performance as informed by Pekrun's control-value theory of emotions. Undergraduates from various disciplines (N ϭ 203) completed domaingeneral, self-report measures in the fall and winter semesters with sessional GPAs evaluated over a 2-year period. Results from cross-lagged and latent growth structural equation models illustrate the benefits of mastery-approach goals for students' emotions, with performance-approach goals predicting less critical thinking, and performance-avoidance goals predicting more anxiety, boredom, critical thinking, and lower achievement gains. Whereas enjoyment was beneficial for most learning strategies, boredom predicted poorer time management, and anxiety predicted lower selfmonitoring and overall GPA. Learning strategies impacted subsequent achievement only in the second semester, with elaboration predicting achievement gains and critical thinking predicting lower overall GPA. Study results provide empirical support for Pekrun's control-value theory and achievement goal theory in showing both direct and indirect effects of students' goals on academic achievement through their emotions and learning strategies.

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“…Moreover, where bivariate relationships between related constructs are incorporated, these are in general too weak to suggest the consolidation into a single construct. This is true for incremental and entity positions, where correlations range from an exceptional -.19 (Malmberg and Little 2007), via -.52 (Chen and Pajares 2010), -.55 (Dupeyrat and Mariné 2005), -.58 (Chen 2012) and -.61 (Howell andBuro 2009) to -.74 (Elliott andMcGregor 2001) and -.78 (Bråten and Strømsø 2004). And this suggests to be even more true for negative and positive effort beliefs, where associations seem to be even weaker (but published empirical studies absent).…”

Section: Introductionmentioning

confidence: 98%

The Pivotal Role of Effort Beliefs in Mediating Implicit Theories of Intelligence and Achievement Goals and Academic Motivations

et al. 2014

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Abstract:Empirical studies into meaning systems surrounding implicit theories of intelligence typically entail two stringent assumptions: that different implicit theories and different effort beliefs represent opposite poles on a single scale, and that implicit theories directly impact the constructs as achievement goals and academic motivations. Through an empirical study based on a large sample of university students, we aim to demonstrate that relaxing these stringent assumptions, and thereby using the meaning system framework to its full potential, will provide strong insights: effort beliefs are crucial mediators of relationships between implicit theories and achievement goals and academic motivations, and the different poles of implicit theories and effort beliefs do expose different relationships with goal setting behaviour and academic motivations. A structural equation model, cross-validated by demonstrating gender-invariance of path coefficients, demonstrates that incremental and entity theory views have less predictive power than positive and negative effort beliefs in explaining achievement goals and motivations. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation AbstractEmpirical studies into meaning systems surrounding implicit theories of intelligence typically entail two stringent assumptions: that different implicit theories and different effort beliefs represent opposite poles on a single scale, and that implicit theories directly impact the constructs as achievement goals and academic motivations. Through an empirical study based on a large sample of university students, we aim to demonstrate that relaxing these stringent assumptions, and thereby using the meaning system framework to its full potential, will provide strong benefits: effort beliefs are crucial mediators of relationships between implicit theories and achievement goals and academic motivations, and the different poles of implicit theories and effort beliefs do expose different relationships with goal setting behaviour and academic motivations. A structural equation model, cross-validated by demonstrating gender-invariance of path coefficients, demonstrates that incremental and entity theory views have less predictive power than positive and negative effort beliefs in explaining achievement goals and motivations.

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Implicit theories, epistemic beliefs, and science motivation: A person-centered approach

Cited by 94 publications

References 80 publications

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course

Achievement goals, emotions, learning, and performance: A process model.

The Pivotal Role of Effort Beliefs in Mediating Implicit Theories of Intelligence and Achievement Goals and Academic Motivations

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