2026-09-20T12:24:38Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1130322026-05-15T19:43:27Zcom_1813_35col_1813_47
Lee, Hansol
Joachims, Thorsten
Kizilcec, Rene
2023-03-31T16:40:32Z
2023-03-31T16:40:32Z
2022-12
ProQuest Submission ID: 11628
ProQuest Publication ID: 29999405
https://hdl.handle.net/1813/113032
https://doi.org/10.7298/hnwn-h665
15644099
35 pages
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.
en
College Admissions
Higher Education
Machine Learning
Prediction
Standardized Tests
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions
dissertation or thesis
https://newcatalog.library.cornell.edu/catalog/15644099
application/pdf
Computer Science
Cornell University
Master of Science
M.S., Computer Science
https://hdl.handle.net/1813/59810.2
Publication
Cornell Theses and Dissertations
Lee, Hansol
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
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<Abstract>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.</Abstract>
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