Toward Cyclic A.I. Modelling of Self-Regulated Learning: A Case Study with E-Learning Trace Data

• URL: http://arxiv.org/abs/2507.02913v1
• Date: Wed, 25 Jun 2025 04:47:53 GMT
• Title: Toward Cyclic A.I. Modelling of Self-Regulated Learning: A Case Study with E-Learning Trace Data
• Authors: Andrew Schwabe, Özgür Akgün, Ella Haig,
• Abstract summary: We apply SRL-informed features to trace data in order to advance modelling of students' SRL activities.<n>We demonstrate that these features improve predictive accuracy and validate the value of further research into cyclic modelling techniques for SRL.
• Score: 0.45060992929802207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many e-learning platforms assert their ability or potential to improve students' self-regulated learning (SRL), however the cyclical and undirected nature of SRL theoretical models represent significant challenges for representation within contemporary machine learning frameworks. We apply SRL-informed features to trace data in order to advance modelling of students' SRL activities, to improve predictability and explainability regarding the causal effects of learning in an eLearning environment. We demonstrate that these features improve predictive accuracy and validate the value of further research into cyclic modelling techniques for SRL.

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