Before and during COVID-19: A Cohesion Network Analysis of students’ online participation in moodle courses

Maria Dorinela Dascalu, Stefan Ruseti, Mihai Dascalu, Danielle S. McNamara, Mihai Carabas, Traian Rebedea, Stefan Trausan-Matu

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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.

Original languageEnglish (US)
Article number106780
JournalComputers in Human Behavior
StatePublished - Aug 2021


  • Click-stream data
  • Cohesion Network Analysis
  • Learner interactions
  • Learning patterns
  • Moodle
  • Sociograms
  • Student behavior

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)


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