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A growing number of college applications has presented an annual challengefor college admissions in the United States. Admission offices havehistorically relied on standardized test scores to organize large applicantpools into viable subsets for review. However, this approach may be subject tobias in test scores and selection bias in test-taking with recent trends towardtest-optional admission. We explore a machine learning-based approach toreplace the role of standardized tests in subset generation while taking intoaccount a wide range of factors extracted from student applications to supporta more holistic review. We evaluate the approach on data from an undergraduateadmission office at a selective US institution (13,248 applications). We findthat a prediction model trained on past admission data outperforms an SAT-basedheuristic and matches the demographic composition of the last admitted class.We discuss the risks and opportunities for how such a learned model could beleveraged to support human decision-making in college admissions.

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