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Conference Paper: Towards personalizing an e-quiz bank for primary school students: an exploration with association rule mining and clustering

TitleTowards personalizing an e-quiz bank for primary school students: an exploration with association rule mining and clustering
Authors
KeywordsAssociation rule mining
Clustering
E-quiz bank
Reading
Issue Date2016
PublisherACM Press. The Conference Proceedings' website is located at http://dl.acm.org/citation.cfm?id=2883851&picked=prox
Citation
The 6th International Conference on Learning Analytics and Knowledge (LAK 2016), Edinburgh, UK., 25-29 April 2016. In Conference Proceedings, 2016, p. 25-29 How to Cite?
AbstractGiven the importance of reading proficiency and habits for young students, an online e-quiz bank, Reading Battle, was launched in 2014 to facilitate reading improvement for primary-school students. With more than ten thousand questions in both English and Chinese, the system has attracted nearly five thousand learners who have made about half a million question answering records. In an effort towards delivering personalized learning experience to the learners, this study aims to discover potentially useful knowledge from learners’ reading and question answering records in the Reading Battle system, by applying association rule mining and clustering analysis. The results show that learners could be grouped into three clusters based on their self-reported reading habits. The rules mined from different learner clusters can be used to develop personalized recommendations to the learners. Implications of the results on evaluating and further improving the Reading Battle system are also discussed.
DescriptionSession: Learner models
Persistent Identifierhttp://hdl.handle.net/10722/232627
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHu, X-
dc.contributor.authorZhang, YF-
dc.contributor.authorChu, SKW-
dc.contributor.authorKe, XB-
dc.date.accessioned2016-09-20T05:31:18Z-
dc.date.available2016-09-20T05:31:18Z-
dc.date.issued2016-
dc.identifier.citationThe 6th International Conference on Learning Analytics and Knowledge (LAK 2016), Edinburgh, UK., 25-29 April 2016. In Conference Proceedings, 2016, p. 25-29-
dc.identifier.isbn978-1-4503-4190-5-
dc.identifier.urihttp://hdl.handle.net/10722/232627-
dc.descriptionSession: Learner models-
dc.description.abstractGiven the importance of reading proficiency and habits for young students, an online e-quiz bank, Reading Battle, was launched in 2014 to facilitate reading improvement for primary-school students. With more than ten thousand questions in both English and Chinese, the system has attracted nearly five thousand learners who have made about half a million question answering records. In an effort towards delivering personalized learning experience to the learners, this study aims to discover potentially useful knowledge from learners’ reading and question answering records in the Reading Battle system, by applying association rule mining and clustering analysis. The results show that learners could be grouped into three clusters based on their self-reported reading habits. The rules mined from different learner clusters can be used to develop personalized recommendations to the learners. Implications of the results on evaluating and further improving the Reading Battle system are also discussed.-
dc.languageeng-
dc.publisherACM Press. The Conference Proceedings' website is located at http://dl.acm.org/citation.cfm?id=2883851&picked=prox-
dc.relation.ispartofProceedings of the Sixth International Conference on Learning Analytics & Knowledge, LAK '16-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsCopyright is held by the owner/author(s)-
dc.subjectAssociation rule mining-
dc.subjectClustering-
dc.subjectE-quiz bank-
dc.subjectReading-
dc.titleTowards personalizing an e-quiz bank for primary school students: an exploration with association rule mining and clustering-
dc.typeConference_Paper-
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.description.naturepostprint-
dc.identifier.doi10.1145/2883851.2883959-
dc.identifier.hkuros263658-
dc.identifier.hkuros264703-
dc.identifier.spage25-
dc.identifier.epage29-
dc.publisher.placeUnited States-

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