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Mining Academic Data to Support Students’ Advisors: A Preliminary Study

dc.contributor.authorEgbers, Lennart
dc.contributor.authorMerceron, Agathe
dc.contributor.authorWagner, Stephan
dc.contributor.editorSchulz, Sandra
dc.date.accessioned2019-10-10T20:08:05Z
dc.date.available2019-10-10T20:08:05Z
dc.date.issued2019
dc.description.abstractMany universities take measures to reduce the number of students dropping out. To support students’ advisors better becomes crucial. Besides their knowledge that they acquire through experience, which is a very important human factor in that process, advisors usually know very little about how students get along in their studies. In this paper, we present preliminary work to support advisors better when meeting students. The current investigation includes two main parts called “overview” and “typical completing behaviours”. The overview part contains visualizations giving general information about how students manage the degree as well as information contrasting students who complete the degree and students who drop out. Typical completing behaviours are obtained through clustering. In this work, data from 2276 students have been analysed.en
dc.identifier.doi10.18420/delfi2019-ws-102
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/27959
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.z
dc.relation.ispartofProceedings of DELFI Workshops 2019
dc.relation.ispartofseriesDELFI
dc.subjectStudents’ advisors
dc.subjectdrop-out students
dc.subjecttime to graduation
dc.subjectinteractive dashboard
dc.subjecttypical completing behaviours.
dc.titleMining Academic Data to Support Students’ Advisors: A Preliminary Studyen
dc.typeText/Conference Poster
gi.citation.publisherPlaceBonn
gi.citation.startPage20
gi.conference.date16.-19. September 2019
gi.conference.locationBerlin
gi.conference.sessiontitleDELFI: Workshop
gi.document.qualitydigidoc

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