Finding traces of self-Regulated learning in activity streams
Date
2018
Authors
Cicchinelli, A.
Veas, E.
Pardo, A.
Pammer-Schindler, V.
Fessl, A.
Barreiros, C.
Lindstädt, S.
Editors
Advisors
Journal Title
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Type:
Conference paper
Citation
Proceedings of the 8th International Conference on Learning Analytics and Knowledge LAK '18, 2018, pp.191-200
Statement of Responsibility
Conference Name
8th International Conference on Learning Analytics and Knowledge (LAK) - Towards User-Centred Learning Analytics (5 Mar 2018 - 9 Mar 2018 : Sydney, AUSTRALIA)
Abstract
This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.
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Dissertation Note
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Copyright 2018 Association for Computing Machinery