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Conference Paper: Enhancing learning paths with concept clustering and rule-based optimization

TitleEnhancing learning paths with concept clustering and rule-based optimization
Authors
KeywordsConcept clustering
Learning path
Ontology analysis
Rule-based optimization
Issue Date2011
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009
Citation
The 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011), Athens, GA., 6-8 July 2011. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, 2011, p. 249-253 How to Cite?
AbstractFinding a good learning path with respect to existing reference paths of closely related concepts is very challenging yet important for effective course teaching and especially adaptive e-learning systems. There are various approaches including ontology analysis to extract the key concepts which could then be correlated to one another using an implicit or explicit knowledge structure for relevant courses. With the available correlation information, an effective optimizer can ultimately return a good learning path according to its predefined objective function. In this paper, we propose to obtain more thorough correlation information through concept clustering, which will then be passed to our rule-based genetic algorithm to search for better learning path(s). To demonstrate the feasibility of our proposal, a prototype of our ontology analyzer enhanced with concept clustering and rule-based optimizer was implemented. Its performance was thoroughly studied and compared favorably against the benchmarking shortest-path optimizer on actual courses. More importantly, our proposal can be easily integrated into existing e-learning systems, and has significant impacts for adaptive or personalized e-learning systems through enhanced ontology analysis. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140241
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorFung, STen_HK
dc.contributor.authorTam, Ven_HK
dc.contributor.authorLam, EYen_HK
dc.date.accessioned2011-09-23T06:09:15Z-
dc.date.available2011-09-23T06:09:15Z-
dc.date.issued2011en_HK
dc.identifier.citationThe 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011), Athens, GA., 6-8 July 2011. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, 2011, p. 249-253en_HK
dc.identifier.isbn978-0-7695-4346-8-
dc.identifier.urihttp://hdl.handle.net/10722/140241-
dc.description.abstractFinding a good learning path with respect to existing reference paths of closely related concepts is very challenging yet important for effective course teaching and especially adaptive e-learning systems. There are various approaches including ontology analysis to extract the key concepts which could then be correlated to one another using an implicit or explicit knowledge structure for relevant courses. With the available correlation information, an effective optimizer can ultimately return a good learning path according to its predefined objective function. In this paper, we propose to obtain more thorough correlation information through concept clustering, which will then be passed to our rule-based genetic algorithm to search for better learning path(s). To demonstrate the feasibility of our proposal, a prototype of our ontology analyzer enhanced with concept clustering and rule-based optimizer was implemented. Its performance was thoroughly studied and compared favorably against the benchmarking shortest-path optimizer on actual courses. More importantly, our proposal can be easily integrated into existing e-learning systems, and has significant impacts for adaptive or personalized e-learning systems through enhanced ontology analysis. © 2011 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009en_US
dc.relation.ispartofProceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011en_HK
dc.rightsIEEE International Conference on Advanced Learning Technologies Proceedings. Copyright © IEEE, Computer Society.-
dc.rights©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectConcept clusteringen_HK
dc.subjectLearning pathen_HK
dc.subjectOntology analysisen_HK
dc.subjectRule-based optimizationen_HK
dc.titleEnhancing learning paths with concept clustering and rule-based optimizationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailTam, V:vtam@eee.hku.hken_HK
dc.identifier.emailLam, EY:elam@eee.hku.hken_HK
dc.identifier.authorityTam, V=rp00173en_HK
dc.identifier.authorityLam, EY=rp00131en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICALT.2011.78en_HK
dc.identifier.scopuseid_2-s2.0-80052713756en_HK
dc.identifier.hkuros194147en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80052713756&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage249en_HK
dc.identifier.epage253en_HK
dc.publisher.placeUnited States-
dc.description.otherThe 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011), Athens, GA., 6-8 July 2011. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, 2011, p. 249-253-
dc.identifier.scopusauthoridFung, ST=36447592700en_HK
dc.identifier.scopusauthoridTam, V=7005091988en_HK
dc.identifier.scopusauthoridLam, EY=7102890004en_HK

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