Abstract：Using machine learning predicts wheel-spinning in online learning. We collected 29,483 primary school students’ log files in an intelligent tutoring system for learning mathematics and distilled the features related to cognition/meta-cognition and actionable features, such as the pause after incorrect, the long pause after hint, and the time of use at home. Seven machine learning models were built and they were explained according to the self-regulated COPES model. The results suggested the random forest algorithm exhibits the best predictive performance and the features in the model fitted the COPES model well. Our study may inspire future theoretical-based intervention studies and alleviate the dilemma that theoretical research lag behind applied research in the field of educational data mining.
龚科，刘玉，张艺红，李俊一. 基于COPES理论预测网络学习中的钻牛角尖[J]. 应用心理学, 2023, 29(6): 503-.
GONG Ke LIU Yu ZHANG Yi-hong LI Jun-yi. Predicting Wheel-spinning in an Online Learning System Based on the COPES Model. 应用心理学, 2023, 29(6): 503-.
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