Paper Number
1469
Paper Type
Complete
Description
This study discusses how students' temporal, adaptive processes, and learning regulation can be understood using multi-channel data. We analyzed the behaviors of 189 students to identify a range of self-regulated learning (SRL) profiles that lead to different achievement in a large-scale undergraduate course seeing how students' SRL unfold during the course, which helps understand the complicated cyclical nature of SRL. We identified three SRL profiles by administrating and analyzing the Motivated Strategies for Learning Questionnaire (MSLQ) three times. We looked at how students adopt different SRL profiles as the course progressed through process mining and clustering techniques to clarify the cyclical nature of SRL. We demonstrated how process mining was used to identify process patterns in self-regulated learning events as captured. Analyzing sequential patterns indicated differences in students' process models. It showed the added value of taking the order of learning activities into account, contributing to theory and practice.
Recommended Citation
Esnaashari, Shadi; Gardner, Lesley A.; and Rehm, Michael, "Exploring the Cyclical Nature of Self-Regulation in Blended Learning: A Longitudinal Study" (2022). ICIS 2022 Proceedings. 3.
https://aisel.aisnet.org/icis2022/learning_iscurricula/learning_iscurricula/3
Exploring the Cyclical Nature of Self-Regulation in Blended Learning: A Longitudinal Study
This study discusses how students' temporal, adaptive processes, and learning regulation can be understood using multi-channel data. We analyzed the behaviors of 189 students to identify a range of self-regulated learning (SRL) profiles that lead to different achievement in a large-scale undergraduate course seeing how students' SRL unfold during the course, which helps understand the complicated cyclical nature of SRL. We identified three SRL profiles by administrating and analyzing the Motivated Strategies for Learning Questionnaire (MSLQ) three times. We looked at how students adopt different SRL profiles as the course progressed through process mining and clustering techniques to clarify the cyclical nature of SRL. We demonstrated how process mining was used to identify process patterns in self-regulated learning events as captured. Analyzing sequential patterns indicated differences in students' process models. It showed the added value of taking the order of learning activities into account, contributing to theory and practice.
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03-Learning