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Experience beyond knowledge: Pragmatic e-learning systems design with learning experience

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dc.contributor.authorKatuk, Norliza-
dc.contributor.authorKim, Jieun-
dc.contributor.authorRyu, Hokyoung-
dc.date.accessioned2022-07-16T10:06:02Z-
dc.date.available2022-07-16T10:06:02Z-
dc.date.created2021-05-12-
dc.date.issued2013-05-
dc.identifier.issn0747-5632-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/162856-
dc.description.abstractWith the growing demand in e-learning system, traditional e-learning systems have dramatically evolved to provide more adaptive ways of learning, in terms of learning objectives, courses, individual learning processes, and so on. This paper reports on differences in learning experience from the learner's perspectives when using an adaptive e-learning system, where the learner's knowledge or skill level is used to configure the learning path. Central to this study is the evaluation of a dynamic content sequencing system (DCSS), with empirical outcomes being interpreted using Csikszentmihalyi's flow theory (i.e., Flow, Boredom, and Anxiety). A total of 80 participants carried out a one-way between-subject study controlled by the type of e-learning system (i.e., the DCSS vs. the non-DCSS). The results indicated that the lower or medium achievers gained certain benefits from the DCSS, whilst the high achievers in learning performance might suffer from boredom when using the DCSS. These contrasting findings can be suggested as a pragmatic design guideline for developing more engaging computer-based learning systems for unsupervised learning situations.-
dc.language영어-
dc.language.isoen-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.titleExperience beyond knowledge: Pragmatic e-learning systems design with learning experience-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Jieun-
dc.contributor.affiliatedAuthorRyu, Hokyoung-
dc.identifier.doi10.1016/j.chb.2012.12.014-
dc.identifier.scopusid2-s2.0-84872449287-
dc.identifier.wosid000316769300030-
dc.identifier.bibliographicCitationCOMPUTERS IN HUMAN BEHAVIOR, v.29, no.3, pp.747 - 758-
dc.relation.isPartOfCOMPUTERS IN HUMAN BEHAVIOR-
dc.citation.titleCOMPUTERS IN HUMAN BEHAVIOR-
dc.citation.volume29-
dc.citation.number3-
dc.citation.startPage747-
dc.citation.endPage758-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaPsychology-
dc.relation.journalWebOfScienceCategoryPsychology, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPsychology, Experimental-
dc.subject.keywordPlusCONTINUANCE INTENTION-
dc.subject.keywordPlusFLOW-
dc.subject.keywordPlusMETACOGNITION-
dc.subject.keywordPlusENVIRONMENTS-
dc.subject.keywordPlusMOTIVATION-
dc.subject.keywordPlusEDUCATION-
dc.subject.keywordPlusQUALITY-
dc.subject.keywordAuthorLearning experience-
dc.subject.keywordAuthorExpert learners-
dc.subject.keywordAuthorNovice learner-
dc.subject.keywordAuthorFlow-
dc.subject.keywordAuthorContent sequencing-
dc.subject.keywordAuthorOptimal flow channel-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0747563212003548?via%3Dihub-
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GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT (DEPARTMENT OF TECHNOLOGY MANAGEMENT)
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