Fair and skill-diverse student group formation via constrained k-way graph partitioning
Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students is a complex task which has to satisfy multiple constraints. This work introduces an unsupervised algorithm for fair and skill-diverse student group formation. This is achieved by taking account of student course marks and sensitive attributes provided by the education office. The skill sets of students are determined using unsupervised dimensionality reduction of course mark data via the Laplacian eigenmap. The problem is formulated as a constrained graph partitioning problem, whereby the diversity of skill sets in each group are maximised, group sizes are upper and lower bounded according to available resources, and `balance' of a sensitive attribute is lower bounded to enforce fairness in group formation. This optimisation problem is solved using integer programming and its effectiveness is demonstrated on a dataset of student course marks from Imperial College London.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeDimensionality ReductionDiversityFairnessgraph partitioningSimilar Papers 제목 키워드 기반
Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem
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 possibl…
AttributeDiversityFairnessScalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS c…
MathMisconceptionsFair-Capacitated Clustering
Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a fair-representation of the groups in term…
ClusteringFairnessDesigning the virtual CAT: A digital tool for algorithmic thinking assessment in compulsory education
Algorithmic thinking (AT) is a critical skill in today's digital society, and it is indispensable not only in computer science-related fields but also in everyday problem-solving. As a foundational component of digital e…
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully des…
BenchmarkingQuestion AnsweringQuestion GenerationQuestion-Generation