Multi-fair Capacitated Students-Topics Grouping Problem

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autoren

  • Tai Le Quy
  • Gunnar Friege
  • Eirini Ntoutsi

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Details

OriginalspracheEnglisch
Titel des SammelwerksAdvances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings
Herausgeber/-innenHisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
Seiten507–519
Seitenumfang13
ISBN (elektronisch)978-3-031-33373-6
PublikationsstatusVeröffentlicht - 27 Mai 2023

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13935 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)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.

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

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Quy, TL, Friege, G & Ntoutsi, E 2023, Multi-fair Capacitated Students-Topics Grouping Problem. in H Kashima, T Ide & W-C Peng (Hrsg.), Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 13935 LNCS, S. 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 (Hrsg.), Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings (S. 507–519). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 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, Hrsg., Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. 2023. S. 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. Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Hrsg. / Hisashi Kashima ; Tsuyoshi Ide ; Wen-Chih Peng. 2023. S. 507–519 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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