{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/113032"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/113032","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions","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_html":"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_has_math":false,"creators":["Lee, Hansol"],"institution":"Cornell University","degree_name":"M.S., Computer Science","degree_level":"Master of Science","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Kizilcec, Rene"],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-24T01:49:10Z","subjects":["College Admissions","Higher Education","Machine Learning","Prediction","Standardized Tests"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/hnwn-h665"],"render_values":[{"text":"https://doi.org/10.7298/hnwn-h665","href":"https://doi.org/10.7298/hnwn-h665","code":true}]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 11628","ProQuest Publication ID: 29999405"],"render_values":[{"text":"ProQuest Submission ID: 11628","href":null,"code":true},{"text":"ProQuest Publication ID: 29999405","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1813/113032","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kizilcec, Rene"]},{"key":"dc:creator","label":"Author","values":["Lee, Hansol"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-31T16:40:32Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-31T16:40:32Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-12"]},{"key":"dc:type","label":"Dc Type","values":["dissertation or thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master of Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S., Computer Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Cornell University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["College Admissions","Higher Education","Machine Learning","Prediction","Standardized Tests"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/hnwn-h665"]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 11628","ProQuest Publication ID: 29999405"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1813/113032"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["35 pages"]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Kizilcec, Rene"],"dc:creator":["Lee, Hansol"],"dc:date.accessioned":["2023-03-31T16:40:32Z"],"dc:date.available":["2023-03-31T16:40:32Z"],"dc:date.issued":["2022-12"],"dc:description":["35 pages"],"dc:description.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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["https://doi.org/10.7298/hnwn-h665"],"dc:identifier.other":["ProQuest Submission ID: 11628","ProQuest Publication ID: 29999405"],"dc:identifier.uri":["https://hdl.handle.net/1813/113032"],"dc:language.iso":["en"],"dc:subject":["College Admissions","Higher Education","Machine Learning","Prediction","Standardized Tests"],"dc:title":["Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions"],"dc:type":["dissertation or thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Master of Science"],"thesis:degree_name":["M.S., Computer Science"],"thesis:institution_name":["Cornell University"]},"updated_at":"2026-07-24T01:49:10Z"}