Measuring Academic Efficiency in High-Impact Scholarships
A Two-Stage Windows DEA and Gaussian Mixture Model Approach
DOI:
https://doi.org/10.7160/eriesj.2026.190105Keywords:
Educational Efficiency, Intersectionality, Data Envelopment Analysis (DEA), Gaussian Mixture Models (GMM), Learning Analytics, Higher EducationAbstract
Evaluating the effectiveness of social support programs in higher education requires moving beyond homogeneous assessments of student performance. This study integrates intersectionality with dynamic efficiency analysis to examine how academic efficiency evolves across diverse student profiles within the Líderes del Mañana full-scholarship program in Mexico. Using a longitudinal dataset of 1,796 students (22,718 student–term observations), we apply a two-stage approach. First, Window Data Envelopment Analysis (DEA) estimates relative academic efficiency over time. Second, Gaussian Mixture Modeling identifies intersectional student profiles based on efficiency trajectories and contextual characteristics. Results reveal five distinct efficiency trajectories. While most students converge toward high-efficiency levels, one cluster exhibits a clear negative efficiency slope, greater variability, and limited institutional alignment, indicating it is a priority for intervention. Other clusters display stable high performance, continuous improvement, or moderate but non-accelerating trajectories. Findings demonstrate that efficiency differences are not explained by single demographic factors but by configurations of social background and institutional context. This study provides a scalable, data-driven framework for aligning equity and efficiency objectives in higher education policy and scholarship programs.
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Copyright (c) 2026 Andres Acero, Miguel Alejandro Garzón-Parra, Jesús Isaac Vázquez-Serrano
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