Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems

Azevedo, Roger; Harley, Jason M.; Trevors, Gregory; Duffy, Melissa C.; Feyzi-Behnagh, Reza; Bouchet, François; Landis, Ronald S.

doi:10.1007/978-1-4419-5546-3_28

Cited by 112 publications

(102 citation statements)

References 61 publications

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“…Finally, from a self-regulatory perspective (e.g., Muis, 2007), and drawing from Pekrun's (2006) model, use of deep learning strategies is theorized to have positive effects on learning outcomes, particularly with complex learning material. Several empirical studies support these hypotheses (e.g., Azevedo & Chauncey Strain, 2011;Azevedo et al, 2013;Murayama, Pekrun, Lichtenfeld, & vom Hofe, 2013;Pekrun et al, 2010.…”

Section: Antecedents and Consequencesmentioning

confidence: 89%

The role of epistemic emotions in mathematics problem solving

Muis

Psaradellis

Lajoie

et al. 2015

Contemporary Educational Psychology

120

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A B S T R A C TThe purpose of this research was to examine the antecedents and consequences of epistemic and activity emotions in the context of complex mathematics problem solving. Seventy-nine elementary students from the fifth grade participated. Students self-reported their perceptions of control and value specific to mathematics problem solving, and were given a complex mathematics problem to solve over a period of several days. At specific time intervals during problem solving, students reported their epistemic and activity emotions. To capture self-regulatory processes, students thought out loud as they solved the problem. Path analyses revealed that both perceived control and value served as important antecedents to the epistemic and activity emotions students experienced during problem solving. Epistemic and activity emotions also predicted the types of processing strategies students used across three phases of selfregulated learning during problem solving. Finally, shallow and deep processing cognitive and metacognitive strategies positively predicted problem-solving performance. Theoretical and educational implications are discussed.

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Section: Antecedents and Consequencesmentioning

confidence: 89%

The role of epistemic emotions in mathematics problem solving

Muis

Psaradellis

Lajoie

et al. 2015

Contemporary Educational Psychology

120

View full text Add to dashboard Cite

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“…The majority of early studies of SRL employed a single approach to observing SRL and thus have been classified as assessing SRL aptitudes or self-regulatory events that unfold during learning (Winne and Perry 2000). However, recent approaches have emphasized the collection and triangulation of multiple sources of cognitive, affective, metacognitive, and motivational data (see Azevedo et al 2013).…”

Section: Methodology and Design Choicesmentioning

confidence: 99%

Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations

Ben-Eliyahu

Bernacki

2015

Metacognition Learning

155

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“…At the other end of this continuum are studies that select a small set of SRL processes and examine them closely in the context of a single learning task. These types of studies usually capture SRL events (Winne and Perry 2000) using process-oriented methodological approaches like microanalytic methods (Cleary 2011), think aloud protocols (Greene et al 2011), and trace methodologies (Aleven et al 2010;Azevedo et al 2013) to observe individual SRL processes as they unfold during learning.…”

Section: Design Choices In Self-regulated Learning Researchmentioning

confidence: 99%

Examining self-efficacy during learning: variability and relations to behavior, performance, and learning

Bernacki

Nokes‐Malach

Aleven

2014

Metacognition Learning

119

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Self-regulated learning (SRL) theorists propose that learners' motivations and cognitive and metacognitive processes interact dynamically during learning, yet researchers typically measure motivational constructs as stable factors. In this study, self-efficacy was assessed frequently to observe its variability during learning and how learners' efficacy related to their problem-solving performance and behavior. Students responded to self-efficacy prompts after every fourth problem of an algebra unit completed in an intelligent tutoring system. The software logged students' problem-solving behaviors and performance. The results of stability and change, path, and correlational analyses indicate that learners' feelings of efficacy varied reliably over the learning task. Their prior performance (i.e., accuracy) predicted subsequent self-efficacy judgments, but this relationship diminished over time as judgments were decreasingly informed by accuracy and increasingly informed by fluency. Controlling for prior achievement and self-efficacy, increases in efficacy during one problemsolving period predicted help-seeking behavior, performance, and learning in the next period. Findings suggest that self-efficacy varies during learning, that students consider multiple aspects of performance to inform their efficacy judgments, and that changes in efficacy influence self-regulated learning processes and outcomes.

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Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems

Cited by 112 publications

References 61 publications

The role of epistemic emotions in mathematics problem solving

The role of epistemic emotions in mathematics problem solving

Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations

Examining self-efficacy during learning: variability and relations to behavior, performance, and learning

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