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Authors: Hana Bydžovská and Michal Brandejs

Affiliation: Faculty of Informatics, Czech Republic

Keyword(s): Recommender System, Social Network Analysis, Data Mining, Prediction, University Information System.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: 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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Paper citation in several formats:
Bydžovská, H. and Brandejs, M. (2014). Towards Student Success Prediction. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR; ISBN 978-989-758-048-2; ISSN 2184-3228, SciTePress, pages 162-169. DOI: 10.5220/0005041701620169

@conference{kdir14,
author={Hana Bydžovská. and Michal Brandejs.},
title={Towards Student Success Prediction},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR},
year={2014},
pages={162-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005041701620169},
isbn={978-989-758-048-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR
TI - Towards Student Success Prediction
SN - 978-989-758-048-2
IS - 2184-3228
AU - Bydžovská, H.
AU - Brandejs, M.
PY - 2014
SP - 162
EP - 169
DO - 10.5220/0005041701620169
PB - SciTePress

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