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dc.contributor.authorGiannakos, Michail
dc.contributor.authorSharma, Kshitij
dc.contributor.authorPappas, Ilias
dc.contributor.authorKostakos, Vassilis
dc.contributor.authorVelloso, Eduardo
dc.date.accessioned2020-01-03T13:39:45Z
dc.date.available2020-01-03T13:39:45Z
dc.date.created2019-03-15T16:39:40Z
dc.date.issued2019
dc.identifier.citationInternational Journal of Information Management. 2019, 48 108-119.nb_NO
dc.identifier.issn0268-4012
dc.identifier.urihttp://hdl.handle.net/11250/2634843
dc.description.abstractMost work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleMultimodal data as a means to understand the learning experiencenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber108-119nb_NO
dc.source.volume48nb_NO
dc.source.journalInternational Journal of Information Managementnb_NO
dc.identifier.doi10.1016/j.ijinfomgt.2019.02.003
dc.identifier.cristin1685215
dc.relation.projectNorges forskningsråd: 255129nb_NO
dc.relation.projectNorges forskningsråd: 290994nb_NO
dc.description.localcode© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.fulltextoriginal
cristin.qualitycode2


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