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Article: Visualizing the learning patterns of topic-based social interaction in online discussion forums: an exploratory study

TitleVisualizing the learning patterns of topic-based social interaction in online discussion forums: an exploratory study
Authors
KeywordsSocial network analysis
Topic modeling
Visualization
Weak ties
Text mining
Issue Date2021
PublisherSpringer New York LLC. The Journal's web site is located at http://www.springer.com/education/learning+&+instruction/journal/11423
Citation
Educational Technology Research and Development, 2021, v. 69 n. 5, p. 2813-2843 How to Cite?
AbstractOnline discussion forums are common features of learning management systems; they allow teachers to engage students in topical discussions in environments beyond physical spaces. This study presents a novel approach to operationalizing the connections between social interaction and contextual topics by visualizing posts in an online discussion forum. Using the weak ties theory, we developed a prototype of a tool that helps visualize the text-based content in online discussion forums, specifically in terms of topic relationships and student interactions. This research unveils a nuanced picture of social and topic connectivity, the nature of social interactions, and the changes in the topics being discussed when serendipity occurs. Our implementation of the tool and the results from testing show that the visualization method was able to determine that the strongly connected major topics in the discussion were related to the intended course learning outcomes, whereas the weakly connected topics could yield insights into students’ unexpected learning. The proposed method of visualization may benefit both teachers and students by helping them to efficiently the learning and teaching process and thus may contribute to formative assessment design, a collaborative learning process, and unexpected learning.
Persistent Identifierhttp://hdl.handle.net/10722/308470
ISSN
2023 Impact Factor: 3.3
2023 SCImago Journal Rankings: 1.706
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWong, GKW-
dc.contributor.authorLi, YK-
dc.contributor.authorLai, X-
dc.date.accessioned2021-12-01T07:53:47Z-
dc.date.available2021-12-01T07:53:47Z-
dc.date.issued2021-
dc.identifier.citationEducational Technology Research and Development, 2021, v. 69 n. 5, p. 2813-2843-
dc.identifier.issn1042-1629-
dc.identifier.urihttp://hdl.handle.net/10722/308470-
dc.description.abstractOnline discussion forums are common features of learning management systems; they allow teachers to engage students in topical discussions in environments beyond physical spaces. This study presents a novel approach to operationalizing the connections between social interaction and contextual topics by visualizing posts in an online discussion forum. Using the weak ties theory, we developed a prototype of a tool that helps visualize the text-based content in online discussion forums, specifically in terms of topic relationships and student interactions. This research unveils a nuanced picture of social and topic connectivity, the nature of social interactions, and the changes in the topics being discussed when serendipity occurs. Our implementation of the tool and the results from testing show that the visualization method was able to determine that the strongly connected major topics in the discussion were related to the intended course learning outcomes, whereas the weakly connected topics could yield insights into students’ unexpected learning. The proposed method of visualization may benefit both teachers and students by helping them to efficiently the learning and teaching process and thus may contribute to formative assessment design, a collaborative learning process, and unexpected learning.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://www.springer.com/education/learning+&+instruction/journal/11423-
dc.relation.ispartofEducational Technology Research and Development-
dc.subjectSocial network analysis-
dc.subjectTopic modeling-
dc.subjectVisualization-
dc.subjectWeak ties-
dc.subjectText mining-
dc.titleVisualizing the learning patterns of topic-based social interaction in online discussion forums: an exploratory study-
dc.typeArticle-
dc.identifier.emailWong, GKW: wongkwg@hku.hk-
dc.identifier.authorityWong, GKW=rp02193-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11423-021-10040-5-
dc.identifier.scopuseid_2-s2.0-85113737678-
dc.identifier.hkuros330516-
dc.identifier.volume69-
dc.identifier.issue5-
dc.identifier.spage2813-
dc.identifier.epage2843-
dc.identifier.isiWOS:000687515600001-
dc.publisher.placeUnited States-

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