A Combinatorial Optimization Framework for Scoring Students in University Admissions

Shao, Lucy; Levine, Richard A.; Hyman, Stefan C.; Stronach, Jeanne; Fan, Juanjuan

doi:10.1177/0193841x221082887

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…In the face of these challenges, colleges are increasingly turning to ML to aid their admissions processes [42]. One common, and potentially fraught, use case is to rank applicants using ML (typically by generating scores corresponding to applicants' predicted chance of admission) and provide the ranking to human reviewers in order to speed up or scale the admissions process [58,62,68]. For example, GRADE, a tool used for graduate admissions at the University of Texas at Austin, was developed because "the number of applications…”

Section: Selective College Admissions: Goals and Challengesmentioning

confidence: 99%

Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability

Lee,

Harvey,

Zhou

et al. 2024

Preprint

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Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges to doing so. First, the number of applications has been steadily growing year-over-year. Second, test-optional policies that have remained in place since the COVID-19 pandemic limit access to key information that has historically been predictive of academic success. Most recently, longstanding debates over affirmative action culminated in the Supreme Court banning race-conscious admissions. Colleges have explored machine learning (ML) models to address the issues of scale and missing test scores, often via ranking algorithms intended to allow human reviewers to focus attention on ‘top’ applicants. However, the Court’s ruling will force changes to these models, which were previously able to consider race as a factor in ranking. There is currently a poor understanding of how these mandated changes will shape applicant ranking algorithms, and, by extension, admitted classes. We seek to address this by quantifying the impact of different admission policies on the applications prioritized for review. We show that removing race data from a previously developed applicant ranking algorithm reduces the diversity of the top-ranked pool of applicants without meaningfully increasing the academic merit of that pool. We contextualize this impact by showing that excluding data on applicant race has a greater impact than excluding other potentially informative variables like intended majors. Finally, we measure the impact of policy change on individuals by comparing the arbitrariness in applicant rank attributable to policy change to the arbitrariness attributable to randomness (i.e., how much an applicant’s rank changes across models that use the same policy but are trained on bootstrapped samples from the same dataset). We find that any given policy has a high degree of arbitrariness (i.e. at most 9% of applicants are consistently ranked in the top 20%), and that removing race data from the ranking algorithm increases arbitrariness in outcomes for most applicants.

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