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

TL;DR

This paper reviews clustering models in educational data science, emphasizing fairness constraints to ensure unbiased decision-making and support educational activities, highlighting recent advances and practical applications.

Contribution

It provides a comprehensive survey of fair clustering models in educational data science, focusing on their application in educational activities and fairness considerations.

Findings

Identifies key fair clustering models used in education.

Highlights the importance of fairness constraints in educational clustering.

Discusses practical applications of fair clustering in educational settings.

Abstract

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.