Deploying Fair and Efficient Course Allocation Mechanisms

2025-02-14

AI Summary

This study addresses large-scale course allocation in higher education, balancing student preferences, classroom capacity constraints, and temporal conflicts to enhance both fairness and efficiency. We propose the first Yankee Swap variant adapted for multi-copy resources—where multiple identical course sections exist—and rigorously evaluate it alongside integer linear programming, serial dictatorship, and round-robin mechanisms. To support reproducible research, we construct the largest publicly available student preference dataset (>1,000 real-world preference profiles) and release an open-source synthetic preference generator. Evaluation employs empirical surveys, realistic simulation, and multi-dimensional fairness metrics—including envy-freeness up to one good (EF1), Pareto optimality (PO), and social welfare. In a real-world deployment on the University of Massachusetts Amherst’s Fall 2024 Computer Science course schedule, our improved Yankee Swap achieves superior overall performance in both fairness and efficiency, significantly outperforming the institution’s current allocation mechanism.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• fair course allocation
• student preferences optimization
• algorithmic frameworks evaluation

Innovation

• Develops synthetic student preference generator.
• Implements four course allocation algorithms.
• Enhances Yankee Swap for item multiplicities.