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Smart Learning Services Based on Smart Cloud Computing

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dc.contributor.authorKim, Svetlana-
dc.contributor.authorSong, Su-Mi-
dc.contributor.authorYoon, Yong-Ik-
dc.date.available2021-02-22T13:16:47Z-
dc.date.created2020-09-03-
dc.date.issued2011-08-
dc.identifier.issn1424-8220-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/12528-
dc.description.abstractContext-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user's behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)-smart pull, smart prospect, smart content, and smart push-concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users' needs by collecting and analyzing users' behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users' behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users.-
dc.language영어-
dc.language.isoen-
dc.language.isoen-
dc.publisherMDPI-
dc.titleSmart Learning Services Based on Smart Cloud Computing-
dc.typeArticle-
dc.contributor.affiliatedAuthorYoon, Yong-Ik-
dc.identifier.doi10.3390/s110807835-
dc.identifier.scopusid2-s2.0-80052171357-
dc.identifier.wosid000294253900031-
dc.identifier.bibliographicCitationSENSORS, v.11, no.8, pp.7835 - 7850-
dc.relation.isPartOfSENSORS-
dc.citation.titleSENSORS-
dc.citation.volume11-
dc.citation.number8-
dc.citation.startPage7835-
dc.citation.endPage7850-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryChemistry, Analytical-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.subject.keywordAuthorcloud computing-
dc.subject.keywordAuthorcontext-awareness-
dc.subject.keywordAuthorsmart learning service-
dc.subject.keywordAuthore-learning-
dc.subject.keywordAuthorontology-
dc.identifier.urlhttps://www.mdpi.com/1424-8220/11/8/7835-
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