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.
翻译:集体工作是教育环境中的一种普遍活动,学生往往根据自己的偏好被分成特定专题群体。分组应尽量反映学生的愿望。通常,由此产生的群体还应在性别或种族等受保护属性方面保持平衡,因为研究表明学生在不同的群体中可能学习得更好。此外,平衡群体主要特征也是各群体之间公平工作量分配的基本要求。在本文中,我们引入了多公平的能力(MFC)分组问题,将学生公平分成非重叠群体,同时确保平衡群体主要属性(约束较低和上限),并在受保护属性方面最大限度地扩大成员的多样性。我们提出了两种办法:超常方法和基于knapsack的方法,以获得MFC分组。关于真实数据集和半合成数据集的实验表明,我们提出的方法可以满足学生的偏好,并在基点和保护属性方面提供平衡和多样化的群体。我们提出的方法可以分别满足学生的偏好。