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dc.contributor.authorSaarela, Mirka
dc.contributor.authorHeilala, Ville
dc.contributor.authorJääskelä, Päivikki
dc.contributor.authorRantakaulio, Anne
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2021-10-07T06:36:43Z
dc.date.available2021-10-07T06:36:43Z
dc.date.issued2021
dc.identifier.citationSaarela, M., Heilala, V., Jääskelä, P., Rantakaulio, A., & Kärkkäinen, T. (2021). Explainable Student Agency Analytics. <i>IEEE Access</i>, <i>9</i>, 137444-137459. <a href="https://doi.org/10.1109/access.2021.3116664" target="_blank">https://doi.org/10.1109/access.2021.3116664</a>
dc.identifier.otherCONVID_101363666
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78049
dc.description.abstractSeveral studies have shown that complex nonlinear learning analytics (LA) techniques outperform the traditional ones. However, the actual integration of these techniques in automatic LA systems remains rare because they are generally presumed to be opaque. At the same time, the current reviews on LA in higher education point out that LA should be more grounded to the learning science with actual linkage to teachers and pedagogical planning. In this study, we aim to address these two challenges. First, we discuss different techniques that open up the decision-making process of complex techniques and how they can be integrated in LA tools. More precisely, we present various global and local explainable techniques with an example of an automatic LA process that provides information about different resources that can support student agency in higher education institutes. Second, we exemplify these techniques and the LA process through recently collected student agency data in four courses of the same content taught by four different teachers. Altogether, we demonstrate how this process—which we call explainable student agency analytics—can contribute to teachers’ pedagogical planning through the LA cycle.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Access
dc.rightsCC BY 4.0
dc.subject.otheroppimisanalytiikka
dc.subject.otherexplainable artificial intelligence
dc.subject.otherdecision making
dc.subject.otherhigher education
dc.subject.otherstudent agency
dc.titleExplainable Student Agency Analytics
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202110075097
dc.contributor.laitosKoulutuksen tutkimuslaitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFinnish Institute for Educational Researchen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineKasvatustiedefi
dc.contributor.oppiaineKoulutusteknologia ja kognitiotiedefi
dc.contributor.oppiaineResurssiviisausyhteisöfi
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningfi
dc.contributor.oppiaineOhjelmisto- ja tietoliikennetekniikkafi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineEducationen
dc.contributor.oppiaineLearning and Cognitive Sciencesen
dc.contributor.oppiaineSchool of Resource Wisdomen
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningen
dc.contributor.oppiaineSoftware and Communications Engineeringen
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange137444-137459
dc.relation.issn2169-3536
dc.relation.volume9
dc.type.versionpublishedVersion
dc.rights.copyright© Authors, 2021
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysooppimisalustat
dc.subject.ysoarviointi
dc.subject.ysotoimijuus
dc.subject.ysopäätöksenteko
dc.subject.ysotekoäly
dc.subject.ysokorkeakouluopetus
dc.subject.ysopalaute
dc.subject.ysokorkeakouluopiskelu
dc.subject.ysoopiskelijat
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26951
jyx.subject.urihttp://www.yso.fi/onto/yso/p7413
jyx.subject.urihttp://www.yso.fi/onto/yso/p2335
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p1246
jyx.subject.urihttp://www.yso.fi/onto/yso/p1236
jyx.subject.urihttp://www.yso.fi/onto/yso/p13164
jyx.subject.urihttp://www.yso.fi/onto/yso/p16486
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/access.2021.3116664
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationThis work was supported by the Academy of Finland and related to the thematic research area Decision Analytics Utilizing Causal Models and Multiobjective Optimization (DEMO) (jyu.fi/demo), University of Jyväskylä, Finland, under Grant 3118


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