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Conference Paper: 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
Issue Date2019
PublisherAssociation for Information Science and Technology (ASIS&T).
Citation
82nd Annual Meeting of the Association for Information Science and Technology (ASIS&T 2019), Melbourne, Australia, 19-23 October 2019 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 (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 experience. 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 page 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 which might have changed students’ collaborative behaviours.
DescriptionPaper Session 1: Algorithmic Trust - Paper Session 1: 3 - Short Papers - ID: 296
Persistent Identifierhttp://hdl.handle.net/10722/275889

 

DC FieldValueLanguage
dc.contributor.authorNg, TD-
dc.contributor.authorHu, X-
dc.contributor.authorLuo, M-
dc.contributor.authorChu, SKW-
dc.date.accessioned2019-09-10T02:51:42Z-
dc.date.available2019-09-10T02:51:42Z-
dc.date.issued2019-
dc.identifier.citation82nd Annual Meeting of the Association for Information Science and Technology (ASIS&T 2019), Melbourne, Australia, 19-23 October 2019-
dc.identifier.urihttp://hdl.handle.net/10722/275889-
dc.descriptionPaper Session 1: Algorithmic Trust - Paper Session 1: 3 - Short Papers - ID: 296-
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 (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 experience. 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 page 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 which might have changed students’ collaborative behaviours.-
dc.languageeng-
dc.publisherAssociation for Information Science and Technology (ASIS&T). -
dc.relation.ispartofProceedings of the 82th Annual Meeting of Association for Information Science and Technology (ASIS&T)-
dc.titleRelations among participation, fairness and performance in collaborative learning with Wiki-based analytics-
dc.typeConference_Paper-
dc.identifier.emailNg, TD: jeremyng@hku.hk-
dc.identifier.emailHu, X: xiaoxhu@hku.hk-
dc.identifier.emailChu, SKW: samchu@hku.hk-
dc.identifier.authorityHu, X=rp01711-
dc.identifier.authorityChu, SKW=rp00897-
dc.identifier.hkuros302656-

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