Machine Learning-Powered Course Allocation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912199957544960 |
|---|---|
| author | Soumalias, Ermis Zamanlooy, Behnoosh Weissteiner, Jakob Seuken, Sven |
| author_facet | Soumalias, Ermis Zamanlooy, Behnoosh Weissteiner, Jakob Seuken, Sven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_00954 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Powered Course Allocation Soumalias, Ermis Zamanlooy, Behnoosh Weissteiner, Jakob Seuken, Sven Computer Science and Game Theory Artificial Intelligence Machine Learning 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. |
| title | Machine Learning-Powered Course Allocation |
| topic | Computer Science and Game Theory Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2210.00954 |