# A review of clustering models in educational data science towards fairness-aware learning

arXiv: https://arxiv.org/abs/2301.03421 · HTML: https://mlsanity.com/paper/2301.03421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 220 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms](https://mlsanity.com/paper/2008.05171/) (2020)
- [Fair Hierarchical Clustering](https://mlsanity.com/paper/2006.10221/) (2020)
- [Automated Clustering and Program Repair for Introductory Programming Assignments](https://mlsanity.com/paper/1603.03165/) (2016)
- [Fairness in Clustering with Multiple Sensitive Attributes](https://mlsanity.com/paper/1910.05113/) (2019)
- [A New Notion of Individually Fair Clustering: α-Equitable k-Center](https://mlsanity.com/paper/2106.05423/) (2021)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Fair-Capacitated Clustering](https://mlsanity.com/paper/2104.12116/) (2021)
- [Privacy-Preserving Synthetic Educational Data Generation](https://mlsanity.com/paper/2207.03202/) (2022)
- [Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education](https://mlsanity.com/paper/2207.01490/) (2022)
- [Evaluation of group fairness measures in student performance prediction problems](https://mlsanity.com/paper/2208.10625/) (2022)
- [Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach](https://mlsanity.com/paper/2111.00901/) (2021)
- [Deep learning for sentence clustering in essay grading support](https://mlsanity.com/paper/2104.11556/) (2021)
- [DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data](https://mlsanity.com/paper/2005.10640/) (2020)

## Built on by (most cited first, 3 of 3)

- [Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten](https://mlsanity.com/paper/2405.16798/) (2024)
- [Anna Karenina Strikes Again: Pre-Trained LLM Embeddings May Favor High-Performing Learners](https://mlsanity.com/paper/2406.06599/) (2024)
- [FACROC: a fairness measure for FAir Clustering through ROC curves](https://mlsanity.com/paper/2503.00854/) (2025)

## Abstract (arXiv)

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.