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Daniel C. Moos, PhD Amanda Miller (Elementary Teacher)

The Self-Regulated Learning Cycle with Hypermedia: Stable Between T asks?. Daniel C. Moos, PhD Amanda Miller (Elementary Teacher). Overview. Introduction Context Theoretical Frameworks Rationale of study Overview of Study Method & procedure Results

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Daniel C. Moos, PhD Amanda Miller (Elementary Teacher)

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  1. The Self-Regulated Learning Cycle with Hypermedia: Stable Between Tasks? Daniel C. Moos, PhD Amanda Miller (Elementary Teacher)

  2. Daniel C. Moos, PhD Department of Education Gustavus Adolphus College AERA 2013 Overview • Introduction • Context • Theoretical Frameworks • Rationale of study • Overview of Study • Method & procedure • Results • Discussion: Theoretical & Methodological implications • Acknowledgements

  3. Daniel C. Moos, PhD Department of Education Gustavus Adolphus College AERA 2013 Context: Hypermedia Learning Non-linear Multiple Representations

  4. Theoretical Frameworks(I) Social Cogntive Approach (Schunk & Zimmerman, 1997; Zimmerman, 2000) )

  5. Theoretical Frameworks(II) Information and Processing Approach (Winne & Hadwin,1998)

  6. Theoretical Frameworks(III) Pintrich (2000) AREAS Prior knowledge activation Time and effort planning Perception of task/context Task interest Monitoring of motivation Monitoring changing context Metacognitive monitoring Monitoring of time, effort Selection of strategies Strategy selection for managing motivation Behavioral strategies, such as help-seeking Contextual choices Cognitive judgments Affective reactions Behavioral reflection Evaluate task/context

  7. Theoretical Frameworks(IV) • Different models, shared assumptions: • Idiosyncratic goals are constructed; self-regulated learning is a proactive, constructive process • Cognition, behavior, and motivation can be potentially monitored and regulated • Behavior is goal-directed and can be modified to achieve a desired goal • “Dynamic”; “Event”; “Recursive” • Empirical support for theoretical assumptions • Differences between and within learners

  8. “Knowledge acquisition” (Moos & Azevedo, 2008)

  9. “Knowledge verification” (Moos & Azevedo, 2008)

  10. Rationale • SRL highly predictive of learning outcomes in variety of contexts with various developmental groups (Bembenutty, 2011; Butler, Cartier, Schnellert, 2011; Cleary & Sandars, 2011; Cleary & Platten, 2013; DiBenedetto & Bembenutty, 2013; McPherson & Renwick, 2011; Schunk & Zimmerman, 2013); particularly with hypermedia (Azevedoet al. 2012; Greene et al. 2013; Moos& Stewart, 2013) • Differences between students’ SRL and individual changes within learning tasks • Stability of SRL processes across tasks for individual students?

  11. Daniel C. Moos, PhD Department of Education Gustavus Adolphus College AERA 2013 Research Questions

  12. Participants & Measures • Participants (N = 37) • Pre-service teachers from a Midwest college • 32 females (86%) and 5 females (14%) • Measures • Mental Model Essays (Azevedo & Cromley, 2005; Chi, 2005): Prior domain knowledge and learning outcomes for two topics • Motivated Strategies for Learning Questionnaire(MSLQ; Pintrich et al., 1991): Self-efficacy, intrinsic motivation, extrinsic motivation, task value, control beliefs • Concurrent Think-Aloud protocol (Ericsson, 2006): SRL during learning (Azevedo & Cromley, 2004; Pintrich, 2000; Winne & Hadwin, 1998; Zimmerman & Schunk, 2001)

  13. Procedure • Procedure for each learning task • Participants individually run • Each participant completed two learning task (order counterbalanced) Pretest Walkthrough & Directions MSLQ Hypermedia (Circulatory/ Constructivism) Posttest Data Prior Knowledge Motivation Learning Outcomes SRL

  14. Results (I) Expectancy X Value (Eccles & Wigfield, 20002)

  15. Results (II)

  16. Discussion • Changes in learning task content can affect first phase of SRL (motivation) • Do changes in the first phase affect subsequent SRL phases? Maybe, Maybe Not

  17. Discussion • Role of Individualized Feedbackthat accounts for the dynamic nature of SRL: “Skill” (capacity) and “Will” (motivation) • What factors affect the dynamic relationship between phases? • Are there more stable, trait-like SRL processes?

  18. Limitations & Future Directions • Methodological challenges: Triangulating with multiple measures and using combination methods (e.g., SRL microanalysis; Cleary, Callan, & Zimmerman, 2012) • Longitudinal data: Some SRL processes change over longer periods of time • Developmental and/or knowledge factors • Sample size

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