Developing early warning systems to predict students’ online learning performance
作者：
Highlights：
• We develop early warning systems to predict at-risk students while a course is in progress.
• Learning portfolios from a fully online course are evaluated by data mining techniques.
• The results show that CART supplemented by AdaBoost has the best classification performance.
• Time-dependent variables are essential to identify student online learning performance.
摘要
•We develop early warning systems to predict at-risk students while a course is in progress.•Learning portfolios from a fully online course are evaluated by data mining techniques.•The results show that CART supplemented by AdaBoost has the best classification performance.•Time-dependent variables are essential to identify student online learning performance.
论文关键词：Learning management system,e-Learning,Early warning system,Data-mining,Learning performance prediction
论文评审过程：Available online 7 May 2014.
论文官网地址：https://doi.org/10.1016/j.chb.2014.04.002
