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Conference Paper: Enhancing educational data mining techniques on online educational resources with a semi-supervised learning approach

TitleEnhancing educational data mining techniques on online educational resources with a semi-supervised learning approach
Authors
KeywordsEducational data mining
Explicit semantic analysis
Knowledge components
Semi-supervised learning
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6337030
Citation
The 4th IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE 2015), United International College, Zhuhai, China, 10-12 December 2015. In Conference Proceedings, 2015, p. 210-213 How to Cite?
AbstractBoth educational data mining (EDM) and learning analytics (LA) focus on applying analytics and data mining techniques to extract useful information from large data sets. EDM is generally more interested in automated methods for discovery within the educational data while LA is relatively keen on applying human-led methods to understand the involved learning processes. Among the various fields of challenging studies in EDM, domain structure discovery is aimed to find the structure of knowledge in an educational domain, such as formulating the prerequisite requirements among various knowledge components through online educational resources. However, with the vast amount of knowledge components in specific subjects, the process of such formulation is very complicated and time-consuming no matter being done manually or semi-automatically. In this work, we propose a systematic framework of a semi-supervised learning approach in which a concept-based classifier is co-trained with an explicit semantic analysis (ESA) classifier to derive a common set of prerequisite rules based on a diverse set of online educational resources. To demonstrate its feasibility, a working prototype is built with some impressive results obtained in specific engineering subjects. More importantly, our proposal sheds light on many possible directions for future exploration. © 2015 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/232270
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTam, VWL-
dc.contributor.authorLam, EYM-
dc.contributor.authorFung, ST-
dc.contributor.authorFok, WWT-
dc.contributor.authorYuen, HK-
dc.date.accessioned2016-09-20T05:28:52Z-
dc.date.available2016-09-20T05:28:52Z-
dc.date.issued2015-
dc.identifier.citationThe 4th IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE 2015), United International College, Zhuhai, China, 10-12 December 2015. In Conference Proceedings, 2015, p. 210-213-
dc.identifier.isbn978-1-4673-9225-9-
dc.identifier.urihttp://hdl.handle.net/10722/232270-
dc.description.abstractBoth educational data mining (EDM) and learning analytics (LA) focus on applying analytics and data mining techniques to extract useful information from large data sets. EDM is generally more interested in automated methods for discovery within the educational data while LA is relatively keen on applying human-led methods to understand the involved learning processes. Among the various fields of challenging studies in EDM, domain structure discovery is aimed to find the structure of knowledge in an educational domain, such as formulating the prerequisite requirements among various knowledge components through online educational resources. However, with the vast amount of knowledge components in specific subjects, the process of such formulation is very complicated and time-consuming no matter being done manually or semi-automatically. In this work, we propose a systematic framework of a semi-supervised learning approach in which a concept-based classifier is co-trained with an explicit semantic analysis (ESA) classifier to derive a common set of prerequisite rules based on a diverse set of online educational resources. To demonstrate its feasibility, a working prototype is built with some impressive results obtained in specific engineering subjects. More importantly, our proposal sheds light on many possible directions for future exploration. © 2015 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6337030-
dc.relation.ispartofIEEE International Conference on Teaching, Assessment and Learning for Engineering Proceedings-
dc.rightsIEEE International Conference on Teaching, Assessment and Learning for Engineering Proceedings. Copyright © IEEE.-
dc.rights©2015 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.subjectEducational data mining-
dc.subjectExplicit semantic analysis-
dc.subjectKnowledge components-
dc.subjectSemi-supervised learning-
dc.titleEnhancing educational data mining techniques on online educational resources with a semi-supervised learning approach-
dc.typeConference_Paper-
dc.identifier.emailTam, VWL: vtam@hkucc.hku.hk-
dc.identifier.emailLam, EYM: elam@eee.hku.hk-
dc.identifier.emailFok, WWT: wilton@hkucc.hku.hk-
dc.identifier.emailYuen, HK: hkyuen@hku.hk-
dc.identifier.authorityTam, VWL=rp00173-
dc.identifier.authorityLam, EYM=rp00131-
dc.identifier.authorityFok, WWT=rp00116-
dc.identifier.authorityYuen, HK=rp00983-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TALE.2015.7386044-
dc.identifier.scopuseid_2-s2.0-84963541941-
dc.identifier.hkuros263442-
dc.identifier.spage210-
dc.identifier.epage213-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161007-

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