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dc.contributor.authorSharma, Kshitij
dc.contributor.authorPapamitsiou, Zacharoula
dc.contributor.authorGiannakos, Michail
dc.date.accessioned2020-01-02T07:21:32Z
dc.date.available2020-01-02T07:21:32Z
dc.date.created2019-09-17T15:19:52Z
dc.date.issued2019
dc.identifier.citationBritish Journal of Educational Technology. 2019, 50 (6), 3004-3031.nb_NO
dc.identifier.issn0007-1013
dc.identifier.urihttp://hdl.handle.net/11250/2634532
dc.description.abstractStudents' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques.nb_NO
dc.language.isoengnb_NO
dc.publisherWileynb_NO
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleBuilding pipelines for educational data using AI and multimodal analytics: A “grey‐box” approachnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber3004-3031nb_NO
dc.source.volume50nb_NO
dc.source.journalBritish Journal of Educational Technologynb_NO
dc.source.issue6nb_NO
dc.identifier.doi10.1111/bjet.12854
dc.identifier.cristin1725785
dc.relation.projectNorges forskningsråd: 290994nb_NO
dc.relation.projectNorges forskningsråd: 255129nb_NO
dc.description.localcode© 2019 The Authors. British Journal of Educational Technology published by John Wiley & Sons Ltd on behalf of British Educational Research Association This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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