Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization

作者：

Highlights：

• Propose a temporal modeling approach for students' dropout behavior in MOOCs.

• Demonstrate the advantage of appended feature modeling space based on PCA over a summed features modeling space.

• Explore the power of the ensemble learning method (stacking generalization) in enhancing the prediction ability.

摘要

•Propose a temporal modeling approach for students' dropout behavior in MOOCs.•Demonstrate the advantage of appended feature modeling space based on PCA over a summed features modeling space.•Explore the power of the ensemble learning method (stacking generalization) in enhancing the prediction ability.

论文关键词：MOOC,Dropout,Prediction,Algorithm,Stacking,Learning analytics

论文评审过程：Received 6 July 2015, Revised 17 November 2015, Accepted 2 December 2015, Available online 4 January 2016, Version of Record 4 January 2016.

论文官网地址：https://doi.org/10.1016/j.chb.2015.12.007