Prediction of academic performance associated with internet usage behaviors using machine learning algorithms
作者：
Highlights：
• New metrics to assess student's academic performance are proposed.
• Real Internet usage data of 4000 undergraduate students were calculated.
• Undergraduate student's academic performance can be differentiated and predicted from Internet usage behaviors.
• Behavior discipline plays a vital role in student's academic success.
• Prediction accuracy generally increases with added features.
摘要
•New metrics to assess student's academic performance are proposed.•Real Internet usage data of 4000 undergraduate students were calculated.•Undergraduate student's academic performance can be differentiated and predicted from Internet usage behaviors.•Behavior discipline plays a vital role in student's academic success.•Prediction accuracy generally increases with added features.
论文关键词：Higher education,Academic performance,Internet usage behaviors,Behavior discipline,Self-control,Machine learning
论文评审过程：Received 30 January 2019, Revised 7 April 2019, Accepted 19 April 2019, Available online 20 April 2019, Version of Record 28 April 2019.
论文官网地址：https://doi.org/10.1016/j.chb.2019.04.015
