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Article: Relations among participation, fairness and performance in collaborative learning with Wiki-based analytics

TitleRelations among participation, fairness and performance in collaborative learning with Wiki-based analytics
Authors
KeywordsComputer-Supported Collaborative Learning (CSCL)
e-learning
Fairness
Learning Analytics
Wiki
Wikiglass
Work Distribution
Issue Date2019
Citation
Proceedings of the Association for Information Science and Technology, 2019, v. 56, n. 1, p. 463-467 How to Cite?
AbstractUtilizing data analytics for supporting collaborative learning is under-studied in secondary education. This study aims to evaluate the effectiveness of Wiki and Wiki-based learning analytics in facilitating collaborative learning in a junior secondary school, in terms of students' participation, contribution, performance, and perception. A Wiki-based learning analytic tool, Wikiglass, was employed for visualizing statistics of students' contributions and participation (e.g., revision counts) in Wiki, on both the group and individual levels. System log, student survey and performance data were collected from students involved in a Wiki-supported inquiry project assignment. An Unfairness Index is proposed to measure students' group work distribution. Results of statistical analyses show that fairness of group work distribution was positively related to active participation in revisions on Wiki on the group level, and the number of sentences with higher-order thinking was related to group performance scores. On the level of individual students, Wiki-based analytics increased the visibility of work distribution and peers' work progress and contributions which might have changed students' collaborative behaviors.
Persistent Identifierhttp://hdl.handle.net/10722/352181

 

DC FieldValueLanguage
dc.contributor.authorNg, Jeremy-
dc.contributor.authorHu, Xiao-
dc.contributor.authorLuo, Miyu-
dc.contributor.authorChu, Sam K.W.-
dc.date.accessioned2024-12-16T03:57:10Z-
dc.date.available2024-12-16T03:57:10Z-
dc.date.issued2019-
dc.identifier.citationProceedings of the Association for Information Science and Technology, 2019, v. 56, n. 1, p. 463-467-
dc.identifier.urihttp://hdl.handle.net/10722/352181-
dc.description.abstractUtilizing data analytics for supporting collaborative learning is under-studied in secondary education. This study aims to evaluate the effectiveness of Wiki and Wiki-based learning analytics in facilitating collaborative learning in a junior secondary school, in terms of students' participation, contribution, performance, and perception. A Wiki-based learning analytic tool, Wikiglass, was employed for visualizing statistics of students' contributions and participation (e.g., revision counts) in Wiki, on both the group and individual levels. System log, student survey and performance data were collected from students involved in a Wiki-supported inquiry project assignment. An Unfairness Index is proposed to measure students' group work distribution. Results of statistical analyses show that fairness of group work distribution was positively related to active participation in revisions on Wiki on the group level, and the number of sentences with higher-order thinking was related to group performance scores. On the level of individual students, Wiki-based analytics increased the visibility of work distribution and peers' work progress and contributions which might have changed students' collaborative behaviors.-
dc.languageeng-
dc.relation.ispartofProceedings of the Association for Information Science and Technology-
dc.subjectComputer-Supported Collaborative Learning (CSCL)-
dc.subjecte-learning-
dc.subjectFairness-
dc.subjectLearning Analytics-
dc.subjectWiki-
dc.subjectWikiglass-
dc.subjectWork Distribution-
dc.titleRelations among participation, fairness and performance in collaborative learning with Wiki-based analytics-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/pra2.48-
dc.identifier.scopuseid_2-s2.0-85075931571-
dc.identifier.volume56-
dc.identifier.issue1-
dc.identifier.spage463-
dc.identifier.epage467-
dc.identifier.eissn2373-9231-

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