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Using data to improve programming instruction

dc.contributor.authorÖztürk, Alisan
dc.contributor.authorBonfert-Taylor, Petra
dc.contributor.authorFügenschuh, Armin
dc.contributor.editorKrömker, Detlef
dc.contributor.editorSchroeder, Ulrik
dc.date.accessioned2019-03-28T08:48:38Z
dc.date.available2019-03-28T08:48:38Z
dc.date.issued2018
dc.description.abstractProgramming classes are difficult by nature and educators are eager to find ways to deal with high dropout rates. Today’s technologies allow us to capture programming-related student data, which can be used to identify students in need of assistance and in getting insights in student learning. In order to assist novice programming students in learning how to program, we developed a web-based programming environment, which is used by students throughout the whole course. While it also provides students with enhanced error messages, all data of students’ interactions are captured. Through this data, we identified two metrics, related to small programming assignments, which highly correlate with student performance. These metrics along other features further enabled us to implement machine learning algorithms that could accurately predict dropout-prone students, early on in the course. Overall, methods of educational data mining can be utilized to assist both, students and educators in introductory programming courses.en
dc.identifier.isbn978-3-88579-678-7
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/21049
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.subjectE-Learning
dc.subjectMachine Learning
dc.subjectIntroductory Programming
dc.subjectPrediction
dc.subjectEnhanced Error Messages
dc.titleUsing data to improve programming instructionen
dc.typeText/Conference Paper
gi.citation.endPage32
gi.citation.publisherPlaceBonn
gi.citation.startPage23
gi.conference.date10.-12. September 2018
gi.conference.locationFrankfurt am Main
gi.conference.sessiontitleBest Paper Session

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