Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions

A growing number of college applications has presented an annual challenge for college admissions in the US. In response to this challenge, admission offices have often relied on standardized test scores to parse their large applicant pools into viable subsets. However, this approach may be subject to bias in test scores and fails to work in test-optional admissions. In this work, we explore a machine learning-based approach to replace the role of standardized tests in subset generation while taking into account a wide range of factors extracted from student applications to support a more holistic review. We evaluate the approach on data from an undergraduate admissions office at a selective US institution and discuss how machine learning can be leveraged to support human decision-making in college admissions.

Name thesis:degree_name

M.S., Computer Science

Level thesis:degree_level

Master of Science

Discipline thesis:degree_discipline

Computer Science

Grantor

Cornell University

Year dc:date.issued

2022

Author dc:creator

• Lee, Hansol

Committee member dc:contributor.committeemember

• Kizilcec, Rene

• College Admissions
• Higher Education
• Machine Learning
• Prediction
• Standardized Tests

Language dc:language.iso

en

DOI

https://doi.org/10.7298/hnwn-h665

Dc Identifier Other

ProQuest Submission ID: 11628
ProQuest Publication ID: 29999405

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/113032