Modelos Predictivos en Python para la Deserción Universitaria: Revisión Sistemática
Predictive Models in Python for University Dropout: A Systematic Review
DOI:
https://doi.org/10.29394/Scientific.issn.2542-2987.2026.11.40.6.93-111Palabras clave:
deserción universitaria, modelos predictivos, python, aprendizaje automático, revisión sistemáticaResumen
La deserción universitaria constituye un fenómeno multidimensional cuya predicción temprana mediante aprendizaje automático se perfila como una estrategia prometedora para las políticas de retención. El objetivo fue sistematizar la evidencia científica sobre modelos predictivos de aprendizaje automático, implementables en Python, para anticipar la deserción universitaria. Se empleó un enfoque cualitativo, método analítico-sintético y diseño de revisión sistemática conforme a PRISMA 2020. La búsqueda se realizó en Google Académico y SciELO, sin acceso a bases de suscripción, sobre publicaciones en español e inglés de 2014 a 2025. De 80 registros identificados, tras eliminar duplicados, cribar por título y resumen y evaluar a texto completo, se incluyeron 16 estudios nuevos que, sumados a 34 referencias de base, conformaron un corpus de 50 referencias temáticas. Los hallazgos muestran que los algoritmos de ensamble, como Random Forest, XGBoost y CatBoost, y las redes neuronales alcanzan una exactitud de entre 77,3% y 96,87%, o un área bajo la curva ROC (AUC) de hasta 0,957, según el contexto institucional, aunque su ventaja sobre la regresión logística no fue uniforme. La calidad del reporte fue alta (4,9 sobre 5), pero la evidencia muestra heterogeneidad metodológica, seis estudios comparten un mismo conjunto de datos y la producción latinoamericana se concentra en Perú y en tesis, sin estudios ecuatorianos. Se concluye que estos modelos son herramientas técnicamente prometedoras para anticipar la deserción universitaria, cuya eficacia depende de articularse con políticas institucionales éticas, transparentes y sostenidas en el tiempo.
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