Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses

Autor/inn/en	Dascalu, Maria-Dorinela; Ruseti, Stefan; Dascalu, Mihai; McNamara, Danielle S.; Carabas, Mihai; Rebedea, Traian
Titel	Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses
Quelle	121 (2021), Artikel 106780 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0747-5632
Schlagwörter	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; Romania + Suchen Sie Ihr Suchwort? School closings; Schule; Schließung; Schließung (von Schulen); Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Schülermitarbeit; Schülermitwirkung; Studentische Mitbestimmung; Student behaviour; Schülerverhalten; Interaktion; Künstliche Intelligenz; Ausland; Natürliche Sprache; Notenspiegel; Anschauungsmaterial; Rumänien
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. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01