Predicting drop-out from social behaviour of students

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.