Project: LernMINT (Data-Driven Learning in STEM Education)
Sub-project: Fair Learning Analytics - Addressing Bias and Discrimination
Project details
Team
- Prof. Dr. Eirini Ntoutsi
- MSc. Tai Le Quy
Project overview
The LernMINT sub-project focuses on fairness and bias in learning analytics systems, particularly in STEM education. As educational data-driven systems increasingly support decision-making, they also risk introducing or amplifying biases that can negatively affect learners.
Specifically, the project investigates:
- how biases emerge in educational data and machine learning models
- how these biases impact different groups of learners
- how fairness-aware methods can be developed and applied in practice
Key contributions and results
The project has delivered several important contributions:
- Development of fairness-aware clustering methods for grouping students under fairness constraints
- Design of evaluation frameworks for assessing bias in student performance prediction
- Comprehensive surveys on datasets and methods for fairness-aware learning
- Applications in STEM education, demonstrating how bias affects learning analytics systems
Publications
- Le Quy, T., Friege, G., & Ntoutsi, E. (2023). Multi-fair Capacitated Students-Topics Grouping Problem. In Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings, Part I (pp. 507-519). Cham: Springer Nature Switzerland.
- Le Quy, T., Friege, G., & Ntoutsi, E. (2023). A review of clustering models in educational data science towards fairness-aware learning . Educational Data Science: Essentials, Approaches, and Tendencies – Proactive Education based on Empirical Big Data Evidence. Springer.
- Le Quy, T., Nguyen, T. H., Friege, G., & Ntoutsi, E. (2023). Evaluation of group fairness measures in student performance prediction problems . In Proceedings of The 7th Workshop on Data Science for Social Good, held in conjunction with ECML PKDD 2022.
- Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasets for fairness-aware machine learning.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,e1452. (Among top cited and top downloaded articles of the Wires DMKD journal in 2022).
- Le Quy, T., Roy, A., Friege, G., & Ntoutsi, E. (2021). Fair-capacitated clustering. In Proceedings of The 14th International Conference on Educational Data Mining (EDM21) (pp. 407-414).
- Le Quy, T., & Ntoutsi, E. (2021). Towards fair, explainable and actionable clustering for learning analytics . In Proceedings of The 14th International Conference on Educational Data Mining (EDM21) (pp.847-851).
For more details, please visit the LernMINT website.
