Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/57638
DC FieldValueLanguage
dc.contributor政大圖檔所en_US
dc.creatorChen, Chih-Ming ; Lee, Hahn-Ming ; Chang, Yu-Jungen_US
dc.creator陳志銘zh_TW
dc.date2008-09en_US
dc.date.accessioned2013-04-18-
dc.date.available2013-04-18-
dc.date.issued2013-04-18-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/57638-
dc.description.abstractPersonalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.en_US
dc.language.isoen_US-
dc.relationComputers & Education, 51(2), 787-814en_US
dc.subjectE-learning System;Genetic Algorithm;Intelligent Tutoring System;Learning Process;Personalized Learning;Web Based Learningen_US
dc.titleIntelligent Web-based Learning System with Personalized Learning Path Guidanceen_US
dc.typearticleen
dc.identifier.doi10.1016/j.compedu.2007.08.004-
dc.doi.urihttp://dx.doi.org/10.1016/j.compedu.2007.08.004-
item.grantfulltextopen-
item.languageiso639-1en_US-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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