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Article: Intelligent agent-based e-learning system for adaptive learning

TitleIntelligent agent-based e-learning system for adaptive learning
Authors
KeywordsAdaptive learning
Aptitude treatment interaction (ATI)
Constructive alignment
E-Learning
Intelligent agent
Multi-agent
Issue Date2011
PublisherIGI Global. The Journal's web site is located at http://www.igi-pub.com/journals/details.asp?id=4295
Citation
International Journal Of Intelligent Information Technologies, 2011, v. 7 n. 3, p. 1-13 How to Cite?
AbstractAdaptive learning approaches support learners to achieve the intended learning outcomes through a personalized way. Previous studies mistakenly treat adaptive e-Learning as personalizing the presentation style of the learning materials, which is not completely correct. The main idea of adaptive learning is to personalize the earning content in a way that can cope with individual differences in aptitude. In this study, an adaptive learning model is designed based on the Aptitude-Treatment Interaction theory and Constructive Alignment Model. The model aims at improving students' learning outcomes through enhancing their intrinsic motivation to learn. This model is operationalized with a multi-agent framework and is validated under a controlled laboratory setting. The result is quite promising. The individual differences of students, especially in the experimental group, have been narrowed significantly. Students who have difficulties in learning show significant improvement after the test. However, the longitudinal effect of this model is not tested in this study and will be studied in the future. Copyright © 2011, IGI Global.
Persistent Identifierhttp://hdl.handle.net/10722/137565
ISSN
2015 SCImago Journal Rankings: 0.242
References

 

DC FieldValueLanguage
dc.contributor.authorLai, Hen_HK
dc.contributor.authorWang, Men_HK
dc.contributor.authorWang, Hen_HK
dc.date.accessioned2011-08-26T14:28:06Z-
dc.date.available2011-08-26T14:28:06Z-
dc.date.issued2011en_HK
dc.identifier.citationInternational Journal Of Intelligent Information Technologies, 2011, v. 7 n. 3, p. 1-13en_HK
dc.identifier.issn1548-3657en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137565-
dc.description.abstractAdaptive learning approaches support learners to achieve the intended learning outcomes through a personalized way. Previous studies mistakenly treat adaptive e-Learning as personalizing the presentation style of the learning materials, which is not completely correct. The main idea of adaptive learning is to personalize the earning content in a way that can cope with individual differences in aptitude. In this study, an adaptive learning model is designed based on the Aptitude-Treatment Interaction theory and Constructive Alignment Model. The model aims at improving students' learning outcomes through enhancing their intrinsic motivation to learn. This model is operationalized with a multi-agent framework and is validated under a controlled laboratory setting. The result is quite promising. The individual differences of students, especially in the experimental group, have been narrowed significantly. Students who have difficulties in learning show significant improvement after the test. However, the longitudinal effect of this model is not tested in this study and will be studied in the future. Copyright © 2011, IGI Global.en_HK
dc.languageengen_US
dc.publisherIGI Global. The Journal's web site is located at http://www.igi-pub.com/journals/details.asp?id=4295en_HK
dc.relation.ispartofInternational Journal of Intelligent Information Technologiesen_HK
dc.subjectAdaptive learningen_HK
dc.subjectAptitude treatment interaction (ATI)en_HK
dc.subjectConstructive alignmenten_HK
dc.subjectE-Learningen_HK
dc.subjectIntelligent agenten_HK
dc.subjectMulti-agenten_HK
dc.titleIntelligent agent-based e-learning system for adaptive learningen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1548-3657&volume=7&issue=3&spage=1&epage=13&date=2011&atitle=Intelligent+agent-based+e-Learning+system+for+adaptive+learning-
dc.identifier.emailWang, M: magwang@hku.hken_HK
dc.identifier.authorityWang, M=rp00967en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.4018/jiit.2011070101en_HK
dc.identifier.scopuseid_2-s2.0-80052860666en_HK
dc.identifier.hkuros187504en_US
dc.identifier.hkuros189152-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80052860666&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue3en_HK
dc.identifier.spage1en_HK
dc.identifier.epage13en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLai, H=24478338800en_HK
dc.identifier.scopusauthoridWang, M=8723779700en_HK
dc.identifier.scopusauthoridWang, H=7501731748en_HK

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