Before and during COVID-19: A Cohesion Network Analysis of students’ online participation in moodle courses
作者：
Highlights：
• We evaluate students' behaviors before and during COVID-19 as a plug-in to Moodle.
• A RNN combines global features with time series analysis to predict student grades.
• Multiple sociograms are generated to observe interaction patterns.
• A significant increase in online participation is observed during the pandemic.
• The prediction model obtained an R2 of 0.27 and 0.34 for the two academic years.
摘要
•We evaluate students' behaviors before and during COVID-19 as a plug-in to Moodle.•A RNN combines global features with time series analysis to predict student grades.•Multiple sociograms are generated to observe interaction patterns.•A significant increase in online participation is observed during the pandemic.•The prediction model obtained an R2 of 0.27 and 0.34 for the two academic years.
论文关键词：Cohesion Network Analysis,Moodle,Click-stream data,Sociograms,Learning patterns,Student behavior,Learner interactions
论文评审过程：Received 20 October 2020, Revised 28 February 2021, Accepted 10 March 2021, Available online 12 March 2021, Version of Record 31 March 2021.
论文官网地址：https://doi.org/10.1016/j.chb.2021.106780
