Investigating the importance of demographic features for EDM-predictions

Cohausz, Lea ; Tschalzev, Andrej ; Bartelt, Christian ; Stuckenschmidt, Heiner

Document Type: Conference or workshop publication
Year of publication: 2023
Book title: Proceedings of the 16th International Conference on Educational Data Mining
Page range: 125-136
Conference title: 16th Educational Data Mining Conference
Location of the conference venue: Bengaluru, India
Date of the conference: 11.-14.07.2023
Publisher: Feng, Mingyu ; Käser, Tanja ; Talukdar, Partha
Place of publication: Bengaluru, India
Publishing house: International Educational Data Mining Society
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data’s sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong fairness concerns. At the same time and despite the frequent use, the value of demographic features for prediction accuracy remains unclear. In this paper, we systematically investigate the importance of demographic features for at-risk prediction using several publicly available datasets from different countries. We find strong evidence that including demographic features does not lead to better-performing models as long as some study-related features exist, such as performance or activity data. Additionally, we show that models, nonetheless, place importance on these features when they are included in the data – although this is not necessary for accuracy. These findings, together with our discussion, strongly suggest that at-risk prediction should not include demographic features. Our code is available at:

Dieser Eintrag ist Teil der Universitätsbibliographie.

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