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ERIC Number: ED537184
Record Type: Non-Journal
Publication Date: 2012-Jun
Pages: 7
Abstractor: As Provided
Reference Count: 17
Predicting Drop-Out from Social Behaviour of Students
Bayer, Jaroslav; Bydzovska, Hana; Geryk, Jan; Obsivac, Tomas; Popelinsky, Lubomir
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012)
This paper focuses on predicting drop-outs and school failures when student data has been enriched with data derived from students social behaviour. These data describe social dependencies gathered from e-mail and discussion board conversations, among other sources. We describe an extraction of new features from both student data and behaviour data represented by a social graph which we construct. Then we introduce a novel method for learning a classifier for student failure prediction that employs cost-sensitive learning to lower the number of incorrectly classified unsuccessful students. We show that the use of social behaviour data results in significant increase of the prediction accuracy. (Contains 2 figures, 7 tables, and 1 footnote.) [This work has been partially supported by the Faculty of Informatics, Masaryk University. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society
Identifiers - Location: Czech Republic