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

Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research

Authors

  • Tai Le Quy
  • Gunnar Friege
  • Eirini Ntoutsi

Research Organisations

External Research Organisations

  • Universität der Bundeswehr München
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Details

Original languageEnglish
Title of host publicationEducational Data Science
Subtitle of host publicationEssentials, Approaches, and Tendencies. Big Data Management
Place of PublicationSingapore
Pages43-94
ISBN (electronic)978-981-99-0026-8
Publication statusPublished - 30 Apr 2023

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.

Keywords

    cs.LG, cs.CY

Cite this

A review of clustering models in educational data science towards fairness-aware learning. / Quy, Tai Le; Friege, Gunnar; Ntoutsi, Eirini.
Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Singapore, 2023. p. 43-94.

Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research

Quy, TL, Friege, G & Ntoutsi, E 2023, A review of clustering models in educational data science towards fairness-aware learning. in Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Singapore, pp. 43-94. https://doi.org/10.48550/arXiv.2301.03421, https://doi.org/10.1007/978-981-99-0026-8_2
Quy, T. L., Friege, G., & Ntoutsi, E. (2023). A review of clustering models in educational data science towards fairness-aware learning. In Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management (pp. 43-94). https://doi.org/10.48550/arXiv.2301.03421, https://doi.org/10.1007/978-981-99-0026-8_2
Quy TL, Friege G, Ntoutsi E. A review of clustering models in educational data science towards fairness-aware learning. In Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Singapore. 2023. p. 43-94 doi: 10.48550/arXiv.2301.03421, 10.1007/978-981-99-0026-8_2
Quy, Tai Le ; Friege, Gunnar ; Ntoutsi, Eirini. / A review of clustering models in educational data science towards fairness-aware learning. Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Singapore, 2023. pp. 43-94
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