基于COPES理论预测网络学习中的钻牛角尖

Predicting Wheel-spinning in an Online Learning System Based on the COPES Model

运用机器学习预测学生在网络学习中的钻牛角尖。在一款学习数学的智能导学系统中采集29483名小学生的操作记录,提取与认知/元认知相关等特征,如答错后的停留时间和求助后长停留等,构建7个预测模型。并基于自我调节学习的COPES理论对模型进行解释。结果表明,岭回归对钻牛角尖的预测效果最好,模型的重要特征与COPES的理论解释相契合。有利于未来开展理论驱动的个性化干预研究,缓解该领域理论滞后于应用的现状。

The present study adopts machine learning to predict wheel-spinning in online learning.Altogether,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 explained according to the self-regulated COPES model.Results suggested that the random forest algorithm exhibited the best predictive performance and the features in the model fitted the COPES model well.To anticipate,our study may inspire future theoretical-based intervention studies and help promote theoretical research in the field of educational data mining.