Entrance exams and first-semester performance: A multi-cohort visual correlation analysis addressing inter-instructor grading bias

Abstract

University entrance exams are widely used to select candidates for higher education, yet their predictive validity remains contested, especially given institutional biases such as grading inconsistencies among instructors. This study assesses the extent to which subject-area scores from entrance exams predict first-semester academic performance at the Universidad Nacional Jorge Basadre Grohmann (UNJBG) in Peru, incorporating a novel visual correlation analysis that accounts for inter-instructor grading bias. Using data from three consecutive admission cohorts (2017–2019) and more than 4,500 students across four academic tracks, section-level Spearman correlations were computed between entrance exam scores and course grades, enabling fairer, bias-controlled insight into performance patterns. The results reveal that certain cognitive domains, particularly Verbal Reasoning and Mathematical Reasoning, show consistent, moderate-to-strong associations with early academic success across tracks, while others demonstrate track-specific relevance. Visualization these relationships, disaggregated by Study Track, uncovers nuanced predictors of academic progression and provides empirical evidence to support more equitable and discipline-sensitive admission policies. This approach contributes both methodologically and practically to the field of educational assessment, emphasizing the role of data visualization in informing institutional decision-making.