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A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University of Eastern Finland, Finland.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: Transforming Learning with Meaningful Technologies: Proceedings / [ed] Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider, Springer, 2019, p. 466-479Conference paper, Published paper (Refereed)
Abstract [en]

Effective collaborative learning is rarely a spontaneous phenomenon. In fact, it requires that a set of conditions are met. Among these central conditions are group formation, size and interaction dynamics. While previous research has demonstrated that size might have detrimental effects on collaborative learning, few have examined how social dynamics develop depending on group size. This learning analytics paper reports on a study that asks: How is group size affecting social dynamics and performance of collaborating students? In contrast to previous research that was mainly qualitative and assessed a limited sample size, our study included 23,979 interactions from 20 courses, 114 groups and 974 students and the group size ranged from 7 to 15 in the context of online problem-based learning. To capture the social dynamics, we applied social network analysis for the study of how group size affects collaborative learning. In general, we conclude that larger groups are associated with decreased performance of individual students, poorer and less diverse social interactions. A high group size led to a less cohesive group, with less efficient communication and less information exchange among members. Large groups may facilitate isolation and inactivity of some students, which is contrary to what collaborative learning is about.

Place, publisher, year, edition, pages
Springer, 2019. p. 466-479
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11722
Keywords [en]
Collaborative learning, Learning analytics, Group size, Social network analysis, Complexity, Interaction analysis, Problem based learning, Medical education
National Category
Human Aspects of ICT
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-177125DOI: 10.1007/978-3-030-29736-7_35ISBN: 978-3-030-29735-0 (print)ISBN: 978-3-030-29736-7 (electronic)OAI: oai:DiVA.org:su-177125DiVA, id: diva2:1379844
Conference
14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-30Bibliographically approved

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