File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Applying an evolutionary approach for learning path optimization in the next-generation e-learning systems

TitleApplying an evolutionary approach for learning path optimization in the next-generation e-learning systems
Authors
KeywordsConcept clustering
Evolutionary optimizers
Hill climbing
Learning path optimization
Issue Date2013
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009
Citation
The IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013), Beijing, China, 15-18 July 2013. In Conference Proceedings, 2013, p. 120-122 How to Cite?
AbstractLearning analytics is targeted to better understand and optimize the process of learning and its environments through the measurement, collection and analysis of learners' data and contexts. To advise people's learning in a specific subject, most intelligent e-learning systems would require course instructors to explicitly input some prior knowledge about the subject such as all the pre-requisite requirements between course modules. Yet human experts may sometimes have conflicting views leading to less desirable learning outcomes. In a previous study, we proposed a complete system framework of learning analytics to perform an explicit semantic analysis on the course materials, followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures, and lastly employing a simple yet efficient evolutionary approach to return the optimal learning sequence. In this paper, we carefully consider to enhance the original evolutionary optimizer with the hill-climbing heuristic, and also critically evaluate the impacts of various experts' recommended learning sequences possibly with conflicting views to optimize the learning paths for the next-generation e-learning systems. More importantly, the integration of heuristics can make our proposed framework more self-adaptive to less structured knowledge domains with conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework for learning analytics. Our empirical evaluation clearly revealed many possible advantages of our proposal with interesting directions for future investigation. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/189846
ISBN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTam, VWLen_US
dc.contributor.authorFung, STen_US
dc.contributor.authorYi, Jen_US
dc.contributor.authorLam, EY-
dc.date.accessioned2013-09-17T15:00:51Z-
dc.date.available2013-09-17T15:00:51Z-
dc.date.issued2013en_US
dc.identifier.citationThe IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013), Beijing, China, 15-18 July 2013. In Conference Proceedings, 2013, p. 120-122en_US
dc.identifier.isbn978-0-7695-5009-1-
dc.identifier.urihttp://hdl.handle.net/10722/189846-
dc.description.abstractLearning analytics is targeted to better understand and optimize the process of learning and its environments through the measurement, collection and analysis of learners' data and contexts. To advise people's learning in a specific subject, most intelligent e-learning systems would require course instructors to explicitly input some prior knowledge about the subject such as all the pre-requisite requirements between course modules. Yet human experts may sometimes have conflicting views leading to less desirable learning outcomes. In a previous study, we proposed a complete system framework of learning analytics to perform an explicit semantic analysis on the course materials, followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures, and lastly employing a simple yet efficient evolutionary approach to return the optimal learning sequence. In this paper, we carefully consider to enhance the original evolutionary optimizer with the hill-climbing heuristic, and also critically evaluate the impacts of various experts' recommended learning sequences possibly with conflicting views to optimize the learning paths for the next-generation e-learning systems. More importantly, the integration of heuristics can make our proposed framework more self-adaptive to less structured knowledge domains with conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework for learning analytics. Our empirical evaluation clearly revealed many possible advantages of our proposal with interesting directions for future investigation. © 2013 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.rightsIEEE International Conference on Advanced Learning Technologies Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2013 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 clustering-
dc.subjectEvolutionary optimizers-
dc.subjectHill climbing-
dc.subjectLearning path optimization-
dc.titleApplying an evolutionary approach for learning path optimization in the next-generation e-learning systemsen_US
dc.typeConference_Paperen_US
dc.identifier.emailTam, VWL: vtam@eee.hku.hken_US
dc.identifier.emailFung, ST: stfung@eee.hku.hken_US
dc.identifier.emailYi, J: alexyi@eee.hku.hken_US
dc.identifier.emailLam, EY: elam@eee.hku.hk-
dc.identifier.authorityTam, VWL=rp00173en_US
dc.identifier.authorityLam, EY=rp00131en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICALT.2013.40-
dc.identifier.scopuseid_2-s2.0-84885194466-
dc.identifier.hkuros222374en_US
dc.identifier.spage120en_US
dc.identifier.epage122en_US
dc.identifier.isiWOS:000333902700033-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 131029-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats