Towards Student Success Prediction

Hana Bydžovská, Michal Brandejs

2014

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.

Paper Citation

in Harvard Style

Bydžovská H. and Brandejs M. (2014). Towards Student Success Prediction . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 162-169. DOI: 10.5220/0005041701620169

in Bibtex Style

@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 - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={162-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005041701620169},
isbn={978-989-758-048-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Towards Student Success Prediction
SN - 978-989-758-048-2
AU - Bydžovská H.
AU - Brandejs M.
PY - 2014
SP - 162
EP - 169
DO - 10.5220/0005041701620169