Cognitive engagement in self-regulated learning: an integrative model

Integrating the two dominant theories of self-regulated learning (SRL) and cognitive engagement could advance our understanding of what makes students more efficient, effective learners. An integration of these theories has yet to be explored, and this paper addresses this gap by proposing a novel i...

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Bibliographic Details
Published in:European journal of psychology of education 2022-09, Vol.37 (3), p.833-852
Main Authors: Li, Shan, Lajoie, Susanne P.
Format: Article
Language:English
Subjects:
Academic Achievement
Cognitive Processes
Education
Educational Environment
Educational Psychology
Learner Engagement
Learning
Learning Analytics
Learning Processes
Learning Strategies
Learning Theories
Pedagogic Psychology
Researchers
Scaffolding (Teaching Technique)
School environment
Self Management
Student participation
Student Responsibility
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Summary:Integrating the two dominant theories of self-regulated learning (SRL) and cognitive engagement could advance our understanding of what makes students more efficient, effective learners. An integration of these theories has yet to be explored, and this paper addresses this gap by proposing a novel integrative model of SRL engagement. Specifically, we identified the nature of cognitive engagement (i.e., changing consecutively, context-dependent, comprising quantitative and qualitative dimensions, occurring consciously or unconsciously), based on which we compared the conceptual differences and similarities between cognitive engagement and SRL. We reviewed three models that have investigated cognitive engagement within the frameworks of SRL, analyzed their features and weaknesses, and proposed an extension of previous models linking SRL and cognitive engagement. The proposed model is one of the first to clarify the mechanisms of how SRL phases and subprocesses relate to the functioning of cognitive engagement. In addition to adding to the theoretical discussions of the relations between cognitive engagement and SRL, the model informs the design of adaptive scaffoldings and the practice of learning analytics. Several recommendations are presented for future research in this area to test this new model empirically.
ISSN:0256-2928
1878-5174
DOI:10.1007/s10212-021-00565-x