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Conference Paper: OP-DCI: A Riskless K-Means Clustering for Influential User Identification in MOOC Forum

TitleOP-DCI: A Riskless K-Means Clustering for Influential User Identification in MOOC Forum
Authors
Issue Date2017
PublisherIEEE.
Citation
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18-21 December 2017 How to Cite?
AbstractMassive Open Online Courses (MOOCs) have recently been highly popular among worldwide learners, while it is challenging to manage and interpret the large-scale discussion forum which is the dominant channel of online communication. K-Means clustering, one of the famous unsupervised learning algorithms, could help instructors identify influential users in MOOC forum, to better understand and improve online learning experience. However, traditional K-Means suffers from bias of outliers and risk of falling into local optimum. In this paper, OP-DCI, an optimized K-Means algorithm is proposed, using outlier post-labeling and distant centroid initialization. Outliers are not solely filtered out but extracted as distinct objects for post-labeling, and distant centroid initialization eliminates the risk of falling into local optimum. With OP-DCI, learners in MOOC forum are clustered efficiently with satisfactory interpretation, and instructors can subsequently design personalized learning strategies for different clusters.
Persistent Identifierhttp://hdl.handle.net/10722/259122
ISBN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHou, X-
dc.contributor.authorLei, CU-
dc.contributor.authorKwok, YK-
dc.date.accessioned2018-09-03T04:01:49Z-
dc.date.available2018-09-03T04:01:49Z-
dc.date.issued2017-
dc.identifier.citation2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18-21 December 2017-
dc.identifier.isbn978-1-5386-1419-8-
dc.identifier.urihttp://hdl.handle.net/10722/259122-
dc.description.abstractMassive Open Online Courses (MOOCs) have recently been highly popular among worldwide learners, while it is challenging to manage and interpret the large-scale discussion forum which is the dominant channel of online communication. K-Means clustering, one of the famous unsupervised learning algorithms, could help instructors identify influential users in MOOC forum, to better understand and improve online learning experience. However, traditional K-Means suffers from bias of outliers and risk of falling into local optimum. In this paper, OP-DCI, an optimized K-Means algorithm is proposed, using outlier post-labeling and distant centroid initialization. Outliers are not solely filtered out but extracted as distinct objects for post-labeling, and distant centroid initialization eliminates the risk of falling into local optimum. With OP-DCI, learners in MOOC forum are clustered efficiently with satisfactory interpretation, and instructors can subsequently design personalized learning strategies for different clusters.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE International Conference on Machine Learning and Applications (ICMLA)-
dc.rightsIEEE International Conference on Machine Learning and Applications (ICMLA). Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleOP-DCI: A Riskless K-Means Clustering for Influential User Identification in MOOC Forum-
dc.typeConference_Paper-
dc.identifier.emailHou, X: hxiangyu@hku.hk-
dc.identifier.emailLei, CU: culei@hku.hk-
dc.identifier.emailKwok, YK: ykwok@hku.hk-
dc.identifier.authorityLei, CU=rp01908-
dc.identifier.authorityKwok, YK=rp00128-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICMLA.2017.00-34-
dc.identifier.hkuros289582-
dc.identifier.isiWOS:000425853000153-
dc.publisher.placeCancun, Mexico-

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