AI 中文总结
研究基于分数招生中重考和分数汇总对成绩的影响，构建战略框架分析单次考试和超级评分政策的利弊，提出三种算法干预措施并通过模拟比较标准评分规则与干预措施。
AI 中文摘要
观察到的标准化考试成绩是一个内生过程的结果：学生通过多次重考策略性地分配努力以提高成绩。由于学生进行这些投入的能力不同，申请人策略与机构评分规则（如广泛使用的单次考试和超级评分政策）之间的相互作用会不同程度地扭曲观察到的分数。我们构建了一个战略框架，学生可根据不同评分政策分配努力。我们发现超级评分通过对噪声抽取进行顺序统计选择引入了系统性的分数膨胀，降低了信号准确性并放大了基于财富的差距。单次考试保留了信号保真度，但排除了缺乏同时准备所有科目的资源的高能力学生。两种规则都不具有统一优势，而是在统计精度和公平结果之间形成了结构性权衡。最后，我们提出了三种算法干预措施，要么修改多次考试分数的组合方式，要么对观察到的分数进行事后校正。通过根据2025年大学理事会数据校准的模拟，我们将标准评分规则与这些提议的干预措施进行了比较。
英文摘要
Observed standardized test scores are the result of an endogenous process: students strategically allocate effort across multiple retake attempts to improve their outcomes. Because students differ in their ability to make these investments, the interaction between applicant strategy and institutional scoring rules---such as the widely used Single-Sitting and Superscoring policies---can disparately distort observed scores. We develop a strategic framework where students allocate effort in response to different scoring policies. We show that Superscoring---the practice of combining the best section scores across attempts---introduces systematic score inflation through order-statistic selection over noise draws. This degrades signal accuracy and amplifies wealth-based disparities by disproportionately rewarding applicants who can afford repeated testing. Conversely, Single-Sitting---which keeps the best overall score rather than section-level scores---preserves signal fidelity but excludes high-ability students who lack the resources to prepare for all subjects simultaneously. Neither rule uniformly dominates; instead, they force a structural trade-off between statistical precision and fair outcomes. Finally, to address this, we propose three algorithmic interventions which either modify how scores from multiple attempts are combined, or apply a post-hoc correction to observed scores. Using simulations calibrated to 2025 College Board data, we compare standard scoring rules against these proposed interventions.
Comments51 pages, 20 figures
