Hey, wake up: Come alongwith the artificial learning companion to the e-learner's outcomes high!

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dc.contributor.authorKim, Hyun Youngko
dc.contributor.authorKim, Bomyeongko
dc.contributor.authorLee, JeeHangko
dc.contributor.authorKim, Jinwooko
dc.date.accessioned2023-06-09T02:02:27Z-
dc.date.available2023-06-09T02:02:27Z-
dc.date.created2023-06-08-
dc.date.issued2017-05-
dc.identifier.citation2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017, pp.1763 - 1770-
dc.identifier.urihttp://hdl.handle.net/10203/307179-
dc.description.abstractCompared to offline learners, online learners' attitude during the learning process is relatively poor, and a feeling of loneliness is entailed as they often study alone. This results in a low learning outcome. So far, no examples exist for the design of a learning companion to this end. Herein we present a pioneering work on a co-existing, artificial learning companion capable of improving the learner's attitude through sleepiness detection. We capture, analyze and estimate the level of sleepiness employing a machine learning technique with the pilot study data. Then, we propose a prototype called LearniCube using a sleepiness detection model with an experimental evaluation of LearniCube.-
dc.languageEnglish-
dc.publisherACM Special Interest Group on Computer-Human Interaction (SIGCHI)-
dc.titleHey, wake up: Come alongwith the artificial learning companion to the e-learner's outcomes high!-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85019608000-
dc.type.rimsCONF-
dc.citation.beginningpage1763-
dc.citation.endingpage1770-
dc.citation.publicationname2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationDenver, CO-
dc.identifier.doi10.1145/3027063.3053123-
dc.contributor.nonIdAuthorKim, Hyun Young-
dc.contributor.nonIdAuthorKim, Bomyeong-
dc.contributor.nonIdAuthorKim, Jinwoo-
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