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.
翻译:机械学习日益支持教育系统和教育数据科学(EDS)领域,从决策支持到教育活动和学习分析。然而,机器学习决定可能会有偏差,因为算法可能会产生基于学生受保护属性的结果,如种族或性别。集群是探索学生数据的重要机学习技术,以支持决策者,并支持集体任务等教育活动。因此,确保高质量集群模式以及满足公平限制是重要的要求。本章全面调查组合模型及其在EDS中的公平性。我们特别侧重于调查教育活动中应用的公平集群模式。这些模型被认为是分析学生数据并确保EDS中的公平性的实际工具。