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 R 2 of 0.27, while the model for the second year obtained a better R 2 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 (R 2 = 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.
Keywords: Click-stream data; Cohesion Network Analysis; Learner interactions; Learning patterns; Moodle; Sociograms; Student behavior.
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