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dc.contributor.authorChen, Weiqin
dc.date.accessioned2020-03-27T12:47:08Z
dc.date.available2020-03-27T12:47:08Z
dc.date.issued2019
dc.PublishedChen W. Knowledge-aware learning analytics for smart learning. Procedia Computer Science. 2019;159:1957-1965eng
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/1956/21614
dc.description.abstractWith the increasing development and adoption of digital technologies for education, more data gathered from educational contexts are being analyzed to give actionable insights to stakeholders. As a data-driven approach for better understanding and optimizing learning and the learning environment, learning analytics has the potential to contribute to smart learning. However, current learning analytics lacks knowledge awareness, an important component in smart learning. This paper draws upon research in the domain of smart learning, reflects on current research on methods and processes in learning analytics, and proposes a framework for knowledge-aware learning analytics for smart learning.en_US
dc.language.isoengeng
dc.publisherElseviereng
dc.rightsAttribution CC BY-NC-NDeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/eng
dc.titleKnowledge-aware learning analytics for smart learningeng
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.date.updated2020-02-07T15:23:23Z
dc.description.versionpublishedVersion
dc.rights.holderCopyright 2019 The Author(s)en_US
dc.identifier.doihttps://doi.org/10.1016/j.procs.2019.09.368
dc.identifier.cristin1789539
dc.source.journalProcedia Computer Science


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