Publication
Which Approach Best Predicts Dropouts in Higher Education?
Kerstin Wagner; Henrik Volkening; Sunay Basyigit; Agathe Merceron; Petra Sauer; Niels Pinkwart
In: Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023). International Conference on Computer Supported Education (CSEDU-2023), April 21-23, Prag, Czech Republic, Pages 15-26, Vol. 2, SCITEPRESS – Science and Technology Publications, 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.