Deploying Fair and Efficient Course Allocation Mechanisms

TL;DR

This paper evaluates and compares different algorithms for fair and efficient course allocation at universities, using a large-scale student preference dataset and formal justice criteria to assess their performance.

Contribution

It introduces a comprehensive evaluation of multiple allocation algorithms, including improvements to Yankee Swap, using the largest publicly available student preference dataset.

Findings

Yankee Swap with improvements performs well under justice criteria.

Serial dictatorship is simple but less fair in certain scenarios.

Synthetic preference generation aids in algorithm testing.

Abstract

Universities regularly face the challenging task of assigning classes to thousands of students while considering their preferences, along with course schedules and capacities. Ensuring the effectiveness and fairness of course allocation mechanisms is crucial to guaranteeing student satisfaction and optimizing resource utilization. We approach this problem from an economic perspective, using formal justice criteria to evaluate different algorithmic frameworks. To evaluate our frameworks, we conduct a large scale survey of university students at University of Massachusetts Amherst, collecting over 1,000 student preferences. This is, to our knowledge, the largest publicly available dataset of student preferences. We develop software for generating synthetic student preferences over courses, and implement four allocation algorithms: the serial dictatorship algorithm used by University of Massachusetts Amherst; Round Robin; an Integer Linear Program; and the Yankee Swap algorithm. We propose improvements to the Yankee Swap framework to handle scenarios with item multiplicities. Through experimentation with the Fall 2024 Computer Science course schedule at University of Massachusetts Amherst, we evaluate each algorithm's performance relative to standard justice criteria, providing insights into fair course allocation in large university settings.