Which Approach Best Predicts Dropouts in Higher Education?

Kerstin Wagner, Henrik Volkening, Sunay Basyigit, Agathe Merceron, Petra Sauer, Niels Pinkwart

2023

Abstract

To predict whether students will drop out of their degree program in a middle-sized German university, we investigate five algorithms — three explainable and two not — along with two different feature sets. It turns out that the models obtained with Logistic Regression (LR), an explainable algorithm, have the best performance. This is an important finding to be able to generate explanations for stakeholders in future work. The models trained with a local feature set and those trained with a global feature set show similar performance results. Further, we study whether the models built with LR are fair with respect to both male and female students as well as the study programs considered in this study. Unfortunately, this is not always the case. This might be due to differences in the dropout rates between subpopulations. This limit should be taken into account in practice.

Paper Citation

in Harvard Style

Wagner K., Volkening H., Basyigit S., Merceron A., Sauer P. and Pinkwart N. (2023). Which Approach Best Predicts Dropouts in Higher Education?. In Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-641-5, SciTePress, pages 15-26. DOI: 10.5220/0011838100003470

in Bibtex Style

@conference{csedu23,
author={Kerstin Wagner and Henrik Volkening and Sunay Basyigit and Agathe Merceron and Petra Sauer and Niels Pinkwart},
title={Which Approach Best Predicts Dropouts in Higher Education?},
booktitle={Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2023},
pages={15-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011838100003470},
isbn={978-989-758-641-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Which Approach Best Predicts Dropouts in Higher Education?
SN - 978-989-758-641-5
AU - Wagner K.
AU - Volkening H.
AU - Basyigit S.
AU - Merceron A.
AU - Sauer P.
AU - Pinkwart N.
PY - 2023
SP - 15
EP - 26
DO - 10.5220/0011838100003470
PB - SciTePress