Machine Learning-Powered Course Allocation
This work solves a specific problem for universities and students by improving welfare and fairness in course scheduling, though it is incremental as it builds on an existing mechanism.
The paper tackles the course allocation problem by addressing students' preference reporting mistakes in the Course Match mechanism, introducing MLCM which uses machine learning for personalized queries to increase average student utility by 7%-11% and minimum utility by 17%-29% with only ten queries.
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. Extensive computational experiments, grounded in real-world data, demonstrate that MLCM, with only ten comparison queries, significantly increases both average and minimum student utility by 7%-11% and 17%-29%, respectively. Finally, we highlight MLCM's robustness to changes in the environment and show how our design minimizes the risk of upgrading to MLCM while making the upgrade process simple for universities and seamless for their students.