Bachelor Theses: "Evaluating fairness measures in student performance prediction"
Predicting students’ academic performance, e.g., grades, the success of courses is one of the main topics of educational data mining . Predictive models have a good forecasting quality but the prediction can be biased w.r.t. some protected attributes, such as gender, race, etc. Fairness in machine learning receives increasing attention  and despite the many definitions of fairness still there is no consensus on which measure is suitable for which application. In the educational domain, several studies [3, 4, 5, 6, 7] evaluate fairness in student performance prediction tasks using measures such as: ABROCA, Equalized Odds, etc. But, there is a lack of an overall evaluation of fairness measures for the education domain.
The goal of this bachelor thesis is to investigate the different fairness measures for student performance prediction problems. A comparative analysis of the different measures including experimental evaluation is required.
An ideal candidate should be:
- a self-motivated and independent learner
- knowledgeable about machine learning (indicated by good grades in related courses)
- experienced with Python The thesis will be supervised by Prof. Eirini Ntoutsi (firstname.lastname@example.org) and PhD candidate Tai Le Quy (email@example.com) from the Institute of Computer Science.References
 Shahiri, Amirah Mohamed, and Wahidah Husain. "A review on predicting student's performance using data mining techniques." Procedia Computer Science 72 (2015): 414-422.
 Ntoutsi, Eirini, et al. "Bias in data‐driven artificial intelligence systems—An introductory survey." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10.3 (2020): e1356.
 Bøyum, Steinar. "Fairness in education–a normative analysis of OECD policy documents." Journal of Education Policy 29.6 (2014): 856-870.
 Gardner, Josh, Christopher Brooks, and Ryan Baker. "Evaluating the fairness of predictive student models through slicing analysis." Proceedings of the 9th International Conference on Learning Analytics & Knowledge. 2019.
 Riazy, Shirin, Katharina Simbeck, and Vanessa Schreck. "Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments." CSEDU (1). 2020.
 Kizilcec, René F., and Hansol Lee. "Algorithmic Fairness in Education." arXiv preprint arXiv:2007.05443 (2020).
 Lee, Hansol, and René F. Kizilcec. "Evaluation of Fairness Trade-offs in Predicting Student Success." arXiv preprint arXiv:2007.00088 (2020).