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

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung

Autoren

  • Tai Le Quy
  • Gunnar Friege
  • Eirini Ntoutsi

Organisationseinheiten

Externe Organisationen

  • Universität der Bundeswehr München
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksEducational Data Science
UntertitelEssentials, Approaches, and Tendencies. Big Data Management
ErscheinungsortSingapore
Seiten43-94
ISBN (elektronisch)978-981-99-0026-8
PublikationsstatusVeröffentlicht - 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.

Zitieren

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. S. 43-94.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung

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, S. 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 (S. 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. S. 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. S. 43-94
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