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dc.contributor.authorGavriushenko, Mariia
dc.contributor.authorSaarela, Mirka
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.editorEscudeiro, Paula
dc.contributor.editorCostagliola, Gennaro
dc.contributor.editorZvacek, Susan
dc.contributor.editorUhomoibhi, James
dc.contributor.editorMcLaren, Bruce M.
dc.date.accessioned2018-08-06T11:13:04Z
dc.date.available2018-08-06T11:13:04Z
dc.date.issued2018
dc.identifier.citationGavriushenko, M., Saarela, M., & Kärkkäinen, T. (2018). Towards Evidence-Based Academic Advising Using Learning Analytics. In P. Escudeiro, G. Costagliola, S. Zvacek, J. Uhomoibhi, & B. M. McLaren (Eds.), <i>Computers Supported Education : 9th International Conference, CSEDU 2017, Porto, Portugal, April 21-23, 2017, Revised Selected Papers</i> (pp. 44-65). Springer. Communications in Computer and Information Science, 865. <a href="https://doi.org/10.1007/978-3-319-94640-5_3" target="_blank">https://doi.org/10.1007/978-3-319-94640-5_3</a>
dc.identifier.otherCONVID_28186890
dc.identifier.otherTUTKAID_78396
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/59124
dc.description.abstractAcademic advising is a process between the advisee, adviser and the academic institution which provides the degree requirements and courses contained in it. Content-wise planning and management of the student’ study path, guidance on studies and academic career support is the main joint activity of advising. The purpose of this article is to propose the use of learning analytics methods, more precisely robust clustering, for creation of groups of actual study profiles of students. This allows academic advisers to provide evidence-based information on the study paths that have actually happened similarly to individual students. Moreover, academic institutions can focus on management and updates of course schedule having an effect of clearly characterized and recognized group of students. Using this approach a model of automated academic advising process, which can determine the study profiles, is presented. The presented model shows the whole automated process, where the learners will be profiled regularly, and where the proper study path will be suggested.fi
dc.format.extent477
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofComputers Supported Education : 9th International Conference, CSEDU 2017, Porto, Portugal, April 21-23, 2017, Revised Selected Papers
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.rightsIn Copyright
dc.subject.otheracademic advising
dc.subject.otherlearning analytics
dc.subject.otherrobust clustering
dc.titleTowards Evidence-Based Academic Advising Using Learning Analytics
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201807313656
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-07-31T12:15:14Z
dc.relation.isbn978-3-319-94639-9
dc.description.reviewstatuspeerReviewed
dc.format.pagerange44-65
dc.relation.issn1865-0929
dc.relation.numberinseries865
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG, part of Springer Nature 2018
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Computer Supported Education
dc.subject.ysoneuvonta
dc.subject.ysokorkea-asteen koulutus
dc.subject.ysooppiminen
dc.subject.ysoanalyysi
dc.subject.ysoanalyysimenetelmät
dc.subject.ysoklusterit
dc.subject.ysoklusterianalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p23
jyx.subject.urihttp://www.yso.fi/onto/yso/p3390
jyx.subject.urihttp://www.yso.fi/onto/yso/p2945
jyx.subject.urihttp://www.yso.fi/onto/yso/p6851
jyx.subject.urihttp://www.yso.fi/onto/yso/p6085
jyx.subject.urihttp://www.yso.fi/onto/yso/p18755
jyx.subject.urihttp://www.yso.fi/onto/yso/p27558
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-319-94640-5_3


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