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Conference Paper: Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization

TitleToward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization
Authors
KeywordsConcept clustering
Learning objects
Learning path optimization
Learning styles
Complete system
Issue Date2012
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009
Citation
The IEEE 12th International Conference on Advanced Learning Technologies (ICALT 2012), Rome, Italy, 4-6 July 2012. In Proceedings of the 12th ICALT, 2012, p. 592-596 How to Cite?
AbstractMost online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/165160
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTam, Ven_US
dc.contributor.authorLam, EYMen_US
dc.contributor.authorFung, STen_US
dc.date.accessioned2012-09-20T08:15:55Z-
dc.date.available2012-09-20T08:15:55Z-
dc.date.issued2012en_US
dc.identifier.citationThe IEEE 12th International Conference on Advanced Learning Technologies (ICALT 2012), Rome, Italy, 4-6 July 2012. In Proceedings of the 12th ICALT, 2012, p. 592-596en_US
dc.identifier.isbn978-0-7695-4702-2-
dc.identifier.urihttp://hdl.handle.net/10722/165160-
dc.description.abstractMost online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation. © 2012 IEEE.-
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.ispartofIEEE International Conference on Advanced Learning Technologies Proceedingsen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectConcept clustering-
dc.subjectLearning objects-
dc.subjectLearning path optimization-
dc.subjectLearning styles-
dc.subjectComplete system-
dc.titleToward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimizationen_US
dc.typeConference_Paperen_US
dc.identifier.emailTam, V: vtam@eee.hku.hken_US
dc.identifier.emailLam, EYM: elam@eee.hku.hken_US
dc.identifier.emailFung, ST: h0609963@hku.hk-
dc.identifier.authorityTam, V=rp00173en_US
dc.identifier.authorityLam, EYM=rp00131en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICALT.2012.66-
dc.identifier.scopuseid_2-s2.0-84867011829-
dc.identifier.hkuros206479en_US
dc.identifier.spage592en_US
dc.identifier.epage596en_US
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130402-

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