The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students’ behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018–2019 when lower fluctuations in participation were observed. The prediction model for the 2018–2019 academic year obtained an R² of 0.27, while the model for the second year obtained a better R² of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance (R² = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic.

COVID-19 大流行改变了整个世界，而在线学习环境的影响和使用也大大增加。本文介绍了新版本的ReaderBench 框架，基于凝聚力网络分析，可用于评估学生的在线活动，作为 Moodle 的插件功能。具有 LSTM 单元的循环神经网络结合了全局特征，包括参与和启动指数，以及对时间框架的时间序列分析，用于预测学生成绩，同时生成多个社会图来观察交互模式。学生的行为和互动在 COVID-19 之前和期间使用 Moodle 以罗马尼亚语进行的算法设计本科课程的连续两个年度实例进行比较。与 2018-2019 学年相比，在观察到参与率波动较低时，COVID-19 爆发导致参与率急剧上升，随后在学期末出现下降。R ²为 0.27，而第二年的模型获得了更好的R ²为 0.34，这个值可以说是由于在线活动量的增加。此外，第一学年的最佳模型可部分推广到第二年，但解释了相当低的方差（R ²  = 0.13）。除了定量分析之外，使用比较社会图对学生行为变化进行的定性分析进一步支持了以下结论，即观察到的学生行为因 COVID-19 大流行而发生了巨大变化。