用学习轨迹数据建模自我调节学习，提升预测与解释能力
Toward Cyclic A.I. Modelling of Self-Regulated Learning: A Case Study with E-Learning Trace Data
- 基于自我调节学习理论设计特征，捕捉学习过程的循环特性
- 所提特征使预测准确率显著提升，验证了循环建模的有效性
- 适合教育人工智能、学习分析领域的研究者参考
许多在线学习平台声称能够提升学生的自我调节学习（SRL），但SRL理论模型的循环性和非定向性给当前机器学习框架的表征带来了重大挑战。本文利用受SRL理论启发的特征对学习轨迹数据进行建模，旨在推进对学生SRL活动的建模，提升在线学习环境中学习行为因果效应的预测性与可解释性。实验表明，这些特征显著提高了预测性能，并验证了进一步研究循环建模技术在SRL中的价值。
原文摘要 · Abstract (English)
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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