  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.
