Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments

Shirin Riazy, Katharina Simbeck, Vanessa Schreck

Abstract

While the current literature on algorithmic fairness has rapidly expanded over the past years, it has yet to fully arrive in educational contexts, namely, learning analytics. In the present paper, we examine possible forms of discrimination, as well as ways to measure and establish fairness in virtual learning environments. The prediction of students’ course outcome is conducted on a VLE dataset and analyzed with respect to fairness. Two measures are recommended for the prior investigation of learning data, to ensure their balance and fitness for further data analysis.

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Paper Citation


in Harvard Style

Riazy S., Simbeck K. and Schreck V. (2020). Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments.In Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-417-6, pages 15-25. DOI: 10.5220/0009324100150025


in Bibtex Style

@conference{csedu20,
author={Shirin Riazy and Katharina Simbeck and Vanessa Schreck},
title={Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2020},
pages={15-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009324100150025},
isbn={978-989-758-417-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments
SN - 978-989-758-417-6
AU - Riazy S.
AU - Simbeck K.
AU - Schreck V.
PY - 2020
SP - 15
EP - 25
DO - 10.5220/0009324100150025