Fairness of In-session Dropout Prediction

Nathalie Rzepka, Katharina Simbeck, Hans-Georg Müller, Niels Pinkwart

2022

Abstract

The increasing use of machine learning models in education is accompanied by some concerns about their fairness. While most research on the fairness of machine learning models in education focuses on discrimination by gender or race, other variables such as parental educational background or home literacy environment are known to impact children's literacy skills too. This paper, therefore, evaluates three different implementations of in-session dropout prediction models used in a learning platform to accompany German school classes with respect to their fairness based on four different fairness measures. We evaluate the models for discrimination of gender, migration background, parental education, and home literacy environment. While predictive parity and equal opportunity are rarely above the defined threshold, predictive equality and slicing analysis indicate that model quality is slightly better for boys, users with higher parental education, users with less than ten books, and users with a migrant background. Furthermore, our analysis of the temporal prediction shows that with increasing accuracy of the model, the fairness decreases. In conclusion, we see that the fairness of a model depends on 1) the fairness measure, 2) the evaluated demographic group and 3) the data with which the model is trained.

Paper Citation

in Harvard Style

Rzepka N., Simbeck K., Müller H. and Pinkwart N. (2022). Fairness of In-session Dropout Prediction. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-562-3, pages 316-326. DOI: 10.5220/0010962100003182

in Bibtex Style

@conference{csedu22,
author={Nathalie Rzepka and Katharina Simbeck and Hans-Georg Müller and Niels Pinkwart},
title={Fairness of In-session Dropout Prediction},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2022},
pages={316-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010962100003182},
isbn={978-989-758-562-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Fairness of In-session Dropout Prediction
SN - 978-989-758-562-3
AU - Rzepka N.
AU - Simbeck K.
AU - Müller H.
AU - Pinkwart N.
PY - 2022
SP - 316
EP - 326
DO - 10.5220/0010962100003182