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dc.contributor.authorHeuer, Hendrik
dc.contributor.authorBreiter, Andreas
dc.contributor.editorKrömker, Detlef
dc.contributor.editorSchroeder, Ulrik
dc.date.accessioned2019-03-28T08:48:35Z
dc.date.available2019-03-28T08:48:35Z
dc.date.issued2018
dc.identifier.isbn978-3-88579-678-7
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21041
dc.description.abstractThis paper explores student’s daily activity in a virtual learning environment in the anonymized Open University Learning Analytics Dataset (OULAD). We show that the daily activity of students can be used to predict their success, i.e. whether they pass or fail a course, with high accuracy. This is important since daily activity can be easily obtained and anonymized. To support this, we show that the binary information whether a student was active on a given day has similar predictive power as a combination of the exact number of clicks on the given day and sensitive private data like gender, disability, and highest educational level. We further show that the anonymized activity data can be used to group students. We identify different student types based on their daily binarized activity and outline how educators and system developers can utilize this to address different learning types. Our primary stakeholders are designers and developers of learning analytics systems as well as those who commission such systems. We discuss the privacy and design implications of our findings for data mining in educational contexts against the background of the principle of data minimization and the General Data Protection Regulation (GDPR) of the European Union.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDeLFI 2018 - Die 16. E-Learning Fachtagung Informatik
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-284
dc.subjectLearning analytics
dc.subjectMOOCs
dc.subjectdaily activity
dc.subjectmachine learning
dc.subjectdata science
dc.subjectgroup formation
dc.subjectdigital traces
dc.subjectprivacy
dc.subjectclickstream
dc.subjectstudent data
dc.subjectstudent performance
dc.titleStudent Success Prediction and the Trade-Off between Big Data and Data Minimizationen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages219-230
mci.conference.sessiontitleLearning Analytics
mci.conference.locationFrankfurt am Main
mci.conference.date10.-12. September 2018


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