Effort Matters in Score-Based Admissions: How Retaking and Aggregation Shape Test Scores

2026-07-28

AI Summary

This study addresses how different scoring policies in standardized testing—such as Single-Sitting versus Superscoring—distort score signals due to strategic retake behavior, thereby exacerbating wealth-based inequities in college admissions. By developing a game-theoretic model integrated with order statistics and calibrated simulations based on 2025 College Board data, the paper provides the first quantitative evidence that Superscoring induces systematic score inflation and delineates the inherent trade-off between signal precision and fairness embedded in scoring rules. Building on these insights, the authors propose three post-processing algorithmic interventions that effectively mitigate both score inflation and inequality, substantially improving the identification rate of high-ability students from resource-constrained backgrounds.

Abstract

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.

Problem

• standardized testing
• score inflation
• retake strategy
• admissions fairness
• scoring policies

Innovation

• score aggregation
• strategic effort allocation
• superscoring
• algorithmic intervention
• admissions fairness