Leveraging the Louvain algorithm for enhanced group formation and collaboration in online learning environments.
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| Title: | Leveraging the Louvain algorithm for enhanced group formation and collaboration in online learning environments. |
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| Authors: | Lee, Minkyung1 (AUTHOR) minklee010@gmail.com, Sharma, Priya1 (AUTHOR) |
| Source: | International Journal of Educational Technology in Higher Education. 12/24/2024, Vol. 21 Issue 1, p1-28. 28p. |
| Subject Terms: | *Internet forums, *Educational technology, *Community education, Group dynamics, Group formation, Virtual communities |
| Abstract: | This study explores the dynamics of student interaction networks within an online asynchronous discussion forum, focusing on both whole group discussions and subgroup interactions distinguished by the Louvain algorithm, a renowned community detection method. Analyzing 2481 posts from 154 undergraduate students across three sections of a communications course centered on discussions about movie clips or social phenomena to enhance media literacy, this research aims to interpret the interaction patterns in these virtual spaces. Traditional methods of group formation, such as teacher intervention and self-selection, often fail to create balanced and effective groups, especially in large online courses. The Louvain algorithm, known for its efficiency in modularity optimization, identifies clusters based on actual student interaction patterns. By leveraging both global and local network analyses, this study provides a comprehensive understanding of interaction structures. The global network analysis offers a macro view of overall interaction structures, revealing diverse patterns despite identical course designs, suggesting the influence of unique group dynamics. The local analysis, focusing on the intricacies of node and edge connections, underscores that the Louvain algorithm's classifications correlate with heightened cohesiveness and collaborative potential. The results indicate that algorithmically detected groups exhibit strong internal communication and cohesiveness, as evidenced by high clustering coefficients, density values, and weighted degrees. These findings underscore the potential of network analysis to optimize online student interactions, providing valuable insights for refining educational design to promote student engagement and collaborative problem-solving. This research highlights the transformative potential of integrating advanced data-driven techniques in educational technology to improve group formation and collaborative learning outcomes, offering empirical insights for educators to enhance online interactions and expand pedagogical understanding. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 181828566 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Leveraging the Louvain algorithm for enhanced group formation and collaboration in online learning environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Minkyung%22">Lee, Minkyung</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> minklee010@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sharma%2C+Priya%22">Sharma, Priya</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 12/24/2024, Vol. 21 Issue 1, p1-28. 28p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Internet+forums%22">Internet forums</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Community+education%22">Community education</searchLink><br /><searchLink fieldCode="DE" term="%22Group+dynamics%22">Group dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Group+formation%22">Group formation</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+communities%22">Virtual communities</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study explores the dynamics of student interaction networks within an online asynchronous discussion forum, focusing on both whole group discussions and subgroup interactions distinguished by the Louvain algorithm, a renowned community detection method. Analyzing 2481 posts from 154 undergraduate students across three sections of a communications course centered on discussions about movie clips or social phenomena to enhance media literacy, this research aims to interpret the interaction patterns in these virtual spaces. Traditional methods of group formation, such as teacher intervention and self-selection, often fail to create balanced and effective groups, especially in large online courses. The Louvain algorithm, known for its efficiency in modularity optimization, identifies clusters based on actual student interaction patterns. By leveraging both global and local network analyses, this study provides a comprehensive understanding of interaction structures. The global network analysis offers a macro view of overall interaction structures, revealing diverse patterns despite identical course designs, suggesting the influence of unique group dynamics. The local analysis, focusing on the intricacies of node and edge connections, underscores that the Louvain algorithm's classifications correlate with heightened cohesiveness and collaborative potential. The results indicate that algorithmically detected groups exhibit strong internal communication and cohesiveness, as evidenced by high clustering coefficients, density values, and weighted degrees. These findings underscore the potential of network analysis to optimize online student interactions, providing valuable insights for refining educational design to promote student engagement and collaborative problem-solving. This research highlights the transformative potential of integrating advanced data-driven techniques in educational technology to improve group formation and collaborative learning outcomes, offering empirical insights for educators to enhance online interactions and expand pedagogical understanding. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s41239-024-00495-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1 Subjects: – SubjectFull: Internet forums Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Community education Type: general – SubjectFull: Group dynamics Type: general – SubjectFull: Group formation Type: general – SubjectFull: Virtual communities Type: general Titles: – TitleFull: Leveraging the Louvain algorithm for enhanced group formation and collaboration in online learning environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Minkyung – PersonEntity: Name: NameFull: Sharma, Priya IsPartOfRelationships: – BibEntity: Dates: – D: 24 M: 12 Text: 12/24/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 23659440 Numbering: – Type: volume Value: 21 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Educational Technology in Higher Education Type: main |
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