Publication details

Towards Student Success Prediction



Year of publication 2014
Type Article in Proceedings
Conference Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System
Description University information systems offer a vast amount of data which potentially contains additional hidden information and relations. Such knowledge can be used to improve the teaching and facilitate the educational process. In this paper, we introduce methods based on a data mining approach and a social network analysis to predict student grade performance. We focus on cases in which we can predict student success or failure with high accuracy. Machine learning algorithms can be employed with the average accuracy of 81.4%. We have defined rules based on grade averages of students and their friends that achieved the precision of 97% and the recall of 53%. We have also used rules based on study-related data where the best two achieved the precision of 96% and the recall was nearly 35%. The derived knowledge can be successfully utilized as a basis for a course enrollment recommender system.
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