Multi-fair Capacitated Students-Topics Grouping Problem

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Authors

  • Tai Le Quy
  • Gunnar Friege
  • Eirini Ntoutsi

Research Organisations

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Details

Original languageEnglish
Title of host publicationProceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023)
EditorsHisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
Pages507–519
Number of pages13
ISBN (electronic)978-3-031-33373-6
Publication statusPublished - 27 May 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13935 LNCS
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Abstract

Group work is a prevalent activity in educational settings, where students are often divided into topic-specific groups based on their preferences. The grouping should reflect students’ aspirations as much as possible. Usually, the resulting groups should also be balanced in terms of protected attributes like gender, as studies suggest that students may learn better in mixed-gender groups. Moreover, to allow a fair workload across the groups, the cardinalities of the different groups should be balanced. In this paper, we introduce a multi-fair capacitated (MFC) grouping problem that fairly partitions students into non-overlapping groups while ensuring balanced group cardinalities (with a lower and an upper bound), and maximizing the diversity of members regarding the protected attribute. To obtain the MFC grouping, we propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. Experimental results on a real dataset and a semi-synthetic dataset show that our proposed methods can satisfy students’ preferences and deliver balanced and diverse groups regarding cardinality and the protected attribute, respectively.

Keywords

    Educational data, Fairness, Grouping, Knapsack, Nash social welfare

ASJC Scopus subject areas

Cite this

Multi-fair Capacitated Students-Topics Grouping Problem. / Quy, Tai Le; Friege, Gunnar; Ntoutsi, Eirini.
Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). ed. / Hisashi Kashima; Tsuyoshi Ide; Wen-Chih Peng. 2023. p. 507–519 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13935 LNCS).

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Quy, TL, Friege, G & Ntoutsi, E 2023, Multi-fair Capacitated Students-Topics Grouping Problem. in H Kashima, T Ide & W-C Peng (eds), Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13935 LNCS, pp. 507–519. https://doi.org/10.1007/978-3-031-33374-3_40
Quy, T. L., Friege, G., & Ntoutsi, E. (2023). Multi-fair Capacitated Students-Topics Grouping Problem. In H. Kashima, T. Ide, & W.-C. Peng (Eds.), Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023) (pp. 507–519). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13935 LNCS). https://doi.org/10.1007/978-3-031-33374-3_40
Quy TL, Friege G, Ntoutsi E. Multi-fair Capacitated Students-Topics Grouping Problem. In Kashima H, Ide T, Peng WC, editors, Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). 2023. p. 507–519. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-33374-3_40
Quy, Tai Le ; Friege, Gunnar ; Ntoutsi, Eirini. / Multi-fair Capacitated Students-Topics Grouping Problem. Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). editor / Hisashi Kashima ; Tsuyoshi Ide ; Wen-Chih Peng. 2023. pp. 507–519 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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