University admission exams can be understood as information systems in which subject areas act as components intended to convey predictive signals about students’ future academic performance. However, the informational effectiveness of these subject areas is rarely evaluated using data-driven approaches. This study proposes a data-driven framework for assessing the informational effectiveness of admission exam subject areas by analyzing their empirical relationships with subsequent academic performance. Institutional data of 2197 students across 33 undergraduate programs from two cohorts after four semesters of study are used. Each academic program is represented as a vector of correlations linking performance in admission subject areas to long-term academic outcomes. The importance of each subject area in the admission exam is contrasted with empirically observed correlations to identify mismatches in informational effectiveness. Additionally, similarity analysis is applied to uncover affinities among academic programs. The results reveal substantial heterogeneity in the informational effectiveness of admission exam subject areas, indicating that predefined subject-area weightings do not consistently reflect their empirical contribution. Similarity patterns further identify groups of programs, suggesting opportunities for program-specific optimization of admission exam design. The proposed framework provides a replicable approach for evaluating and refining admission exams as information systems, contributing to data-driven decision-making in educational assessment design.
Huertas-Condori et al. (Sun,) studied this question.