Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses
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| Title: | Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses |
|---|---|
| Language: | English |
| Authors: | Dascalu, Maria-Dorinela, Ruseti, Stefan, Dascalu, Mihai, McNamara, Danielle S., Carabas, Mihai, Rebedea, Traian |
| Source: | Grantee Submission. 2021 121. |
| Peer Reviewed: | Y |
| Page Count: | 19 |
| Publication Date: | 2021 |
| Sponsoring Agency: | Institute of Education Sciences (ED) Office of Naval Research (ONR) (DOD) |
| Contract Number: | R305A180261 R305A180144 N000141712300 N000141912424 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | COVID-19, Pandemics, Integrated Learning Systems, School Closing, Educational Technology, Technology Uses in Education, Student Participation, Student Behavior, Interaction, Undergraduate Students, Artificial Intelligence, Foreign Countries, Natural Language Processing, Grades (Scholastic), Visual Aids |
| Geographic Terms: | Romania |
| DOI: | 10.1016/j.chb.2021.106780 |
| ISSN: | 0747-5632 |
| Abstract: | 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 is partially generalizable to the second year, but explains a considerably lower variance (R[subscript 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. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2021 |
| Accession Number: | ED616067 |
| Database: | ERIC |
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