A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. However, this approach may be subject to bias in test scores and selection bias in test-taking with recent trends toward test-optional admission. 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 admission office at a selective US institution (13,248 applications). We find that a prediction model trained on past admission data outperforms an SAT-based heuristic and matches the demographic composition of the last admitted class. We discuss the risks and opportunities for how such a learned model could be leveraged to support human decision-making in college admissions.

翻译：美国高校申请数量持续增长，每年都给招生工作带来挑战。招生办公室历来依赖标准化考试成绩，将庞大的申请者群体划分为可供审阅的可行子集。然而，随着近年来越来越多高校实行"考试可选"政策，标准化考试可能存在分数偏差及应试选择偏差等问题。我们探索了一种基于机器学习的方法，在考虑学生申请材料中提取的广泛因素以支持更全面评估的同时，取代标准化考试在申请者子集生成中的作用。我们采用某美国精英院校本科招生办公室的13,248份申请数据对该方法进行验证。研究发现，基于历史录取数据训练出的预测模型在性能上优于基于SAT的启发式方法，且与上一届录取学生的构成比例相匹配。我们讨论了此类学习模型在支持大学招生人工决策过程中存在的风险与机遇。