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，给年度招生工作带来挑战。招生办公室历来依赖标准化考试成绩，将庞大的申请人群划分为可行的审核子集。然而，随着近年转向可选择性提交考试（test-optional）的录取政策，这种方法可能受到考试成绩偏差及考试参与的选择性偏差的影响。我们探索一种基于机器学习的方法，取代标准化考试在子集生成中的角色，同时利用从学生申请中提取的多维度因素，支持更全面的综合评价。我们基于美国某精英院校本科招生办公室的数据（13,248份申请）评估该方法。研究发现，基于历史录取数据训练的预测模型优于以SAT为基础的启发式方法，且与上一届录取学生的群体构成相匹配。我们讨论这种学习型模型在高校招生中辅助人类决策的风险与机遇。