Machine Learning-Powered Course Allocation

arXiv:2210.00954v3 Announce Type: replace-cross
Abstract: We study the course allocation problem, where universities assign course schedules to students. The current state-of-the-art mechanism, Course Match, has one major shortcoming: students make significant mistakes when reporting their preferences, which negatively affects welfare and fairness. To address this issue, we introduce a new mechanism, Machine Learning-powered Course Match (MLCM). At the core of MLCM is a machine learning-powered preference elicitation module that iteratively asks personalized pairwise comparison queries to alleviate students' reporting mistakes. …

abstract art arxiv course cs.ai cs.gt cs.lg current fairness issue machine machine learning major match mistakes reporting state students study type universities welfare