Details
| Original language | English |
|---|---|
| Publication status | E-pub ahead of print - 20 Jun 2022 |
Abstract
Keywords
- cs.LG, cs.CY
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2022.
Research output: Working paper/Preprint › Preprint
}
TY - UNPB
T1 - Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem
AU - Quy, Tai Le
AU - Ntoutsi, Eirini
AU - Friege, Gunnar
N1 - Acknowledgment: The work of the first author is supported by the Ministry of Science and Cul- ture of Lower Saxony, Germany, within the Ph.D. program “LernMINT: Data- assisted teaching in the MINT subjects”.
PY - 2022/6/20
Y1 - 2022/6/20
N2 - 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 the students' aspirations as much as possible. Usually, the resulting groups should also be balanced in terms of protected attributes like gender or race since studies indicate that students might learn better in a diverse group. Moreover, balancing the group cardinalities is also an essential requirement for fair workload distribution across the groups. In this paper, we introduce the multi-fair capacitated (MFC) grouping problem that fairly partitions students into non-overlapping groups while ensuring balanced group cardinalities (with a lower bound and an upper bound), and maximizing the diversity of members in terms of protected attributes. We propose two approaches: a heuristic method and a knapsack-based method to obtain the MFC grouping. The experiments on a real dataset and a semi-synthetic dataset show that our proposed methods can satisfy students' preferences well and deliver balanced and diverse groups regarding cardinality and the protected attribute, respectively.
AB - 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 the students' aspirations as much as possible. Usually, the resulting groups should also be balanced in terms of protected attributes like gender or race since studies indicate that students might learn better in a diverse group. Moreover, balancing the group cardinalities is also an essential requirement for fair workload distribution across the groups. In this paper, we introduce the multi-fair capacitated (MFC) grouping problem that fairly partitions students into non-overlapping groups while ensuring balanced group cardinalities (with a lower bound and an upper bound), and maximizing the diversity of members in terms of protected attributes. We propose two approaches: a heuristic method and a knapsack-based method to obtain the MFC grouping. The experiments on a real dataset and a semi-synthetic dataset show that our proposed methods can satisfy students' preferences well and deliver balanced and diverse groups regarding cardinality and the protected attribute, respectively.
KW - cs.LG
KW - cs.CY
U2 - 10.48550/arXiv.2206.09895
DO - 10.48550/arXiv.2206.09895
M3 - Preprint
BT - Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem
ER -