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Group Formation Based on Extraversion and Prior Knowledge: A Randomized Controlled Study in Higher Education Online

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Title: Group Formation Based on Extraversion and Prior Knowledge: A Randomized Controlled Study in Higher Education Online
Language: English
Authors: Adrienne Mueller (ORCID 0000-0003-4193-5737), Johannes Konert, René Röpke, Ömer Genc, Henrik Bellhäuser
Source: Journal of Computing in Higher Education. 2025 37(3):780-808.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 29
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Extraversion Introversion, Prior Learning, Online Courses, Heterogeneous Grouping, Homogeneous Grouping, Satisfaction, Performance, College Students, Cooperative Learning
DOI: 10.1007/s12528-024-09406-4
ISSN: 1042-1726
1867-1233
Abstract: The study investigates how the 2×2 configuration of homogeneous and heterogeneous distributions of extraversion and prior knowledge influences group outcomes, including satisfaction, performance, and stability. Based on the standard deviation of extraversion and prior knowledge, groups were established to test experimentally, what form of grouping leads to best outcomes. The randomized controlled trial took place in the context of an online course with 355 prospective students, working in 82 groups. The two characteristics extraversion and prior knowledge were distributed algorithmically, either homogeneously or heterogeneously. Results showed no superiority of heterogeneous formation, yet there were systematic interaction effects by the experimental group formation on satisfaction and performance. Due to the increasing relevance of online groupwork, explorative results are reported and integrated. Ideas for future research on group formation as an important influencing factor are discussed. Findings supports knowledge about cooperative online learning by optimizing the selection of group members using a therefore implemented algorithm.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480095
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Adrienne+Mueller%22">Adrienne Mueller</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4193-5737">0000-0003-4193-5737</externalLink>)<br /><searchLink fieldCode="AR" term="%22Johannes+Konert%22">Johannes Konert</searchLink><br /><searchLink fieldCode="AR" term="%22René+Röpke%22">René Röpke</searchLink><br /><searchLink fieldCode="AR" term="%22Ömer+Genc%22">Ömer Genc</searchLink><br /><searchLink fieldCode="AR" term="%22Henrik+Bellhäuser%22">Henrik Bellhäuser</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: The study investigates how the 2×2 configuration of homogeneous and heterogeneous distributions of extraversion and prior knowledge influences group outcomes, including satisfaction, performance, and stability. Based on the standard deviation of extraversion and prior knowledge, groups were established to test experimentally, what form of grouping leads to best outcomes. The randomized controlled trial took place in the context of an online course with 355 prospective students, working in 82 groups. The two characteristics extraversion and prior knowledge were distributed algorithmically, either homogeneously or heterogeneously. Results showed no superiority of heterogeneous formation, yet there were systematic interaction effects by the experimental group formation on satisfaction and performance. Due to the increasing relevance of online groupwork, explorative results are reported and integrated. Ideas for future research on group formation as an important influencing factor are discussed. Findings supports knowledge about cooperative online learning by optimizing the selection of group members using a therefore implemented algorithm.
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