Predicting at-risk university students in a virtual learning environment via a machine learning algorithm

作者：

Highlights：

• Proposed algorithm predicts both marginal and at-risk university students.

• Training vectors reduce by more than 59% while maintaining the prediction accuracy.

• Overall accuracy of 91.3–93.5% in predicting marginal university students.

• Overall accuracy of 92.2–93.8% in predicting at-risk university students.

摘要

•Proposed algorithm predicts both marginal and at-risk university students.•Training vectors reduce by more than 59% while maintaining the prediction accuracy.•Overall accuracy of 91.3–93.5% in predicting marginal university students.•Overall accuracy of 92.2–93.8% in predicting at-risk university students.

论文关键词：Academic performance,At-risk students,Event prediction,Higher education,Machine learning,Virtual learning environments

论文评审过程：Received 19 December 2017, Accepted 25 June 2018, Available online 27 June 2018, Version of Record 6 April 2020.

论文官网地址：https://doi.org/10.1016/j.chb.2018.06.032