Integración de datos educativos y aprendizaje automático para la predicción del rendimiento académico
Este artículo presenta los resultados de la investigación “Integración de Datos Educativos y Técnicas de Aprendizaje de Máquina para la Predicción del Desempeño Académico Estudiantil”, desarrollada en la Universidad Distrital Francisco José de Caldas entre los años 2021 y 2023. El estudio parte de la premisa de que la información académica, cuando es procesada mediante herramientas analíticas adecuadas, puede utilizarse no solo para predecir el rendimiento académico, sino también para apoyar acciones preventivas y correctivas orientadas a mejorar el desempeño de los estudiantes.
El objetivo principal fue predecir el rendimiento académico en tres programas de ingeniería mediante técnicas de aprendizaje automático, a partir de un conjunto de aproximadamente 7.000 registros estudiantiles. En cada corrida experimental se analizaron alrededor de 325 variables, aplicando métodos de selección de características en cada semestre para identificar aquellas con mayor influencia en el desempeño. Este proceso permitió identificar un conjunto de variables comunes que impactan el rendimiento estudiantil, independientemente del tipo de ingeniería.
La capacidad predictiva se evaluó mediante algoritmos de aprendizaje supervisado (SVC, KNN, árbol de decisión y LDA), así como mediante enfoques de ensamblado que incluyeron métodos de Bagging (Random Forest, ExtraTreesClassifier), Boosting (AdaBoost, GBM, XGBoost, CatBoost y LightGBM) y estrategias de Voting (Blending y Stacking). El modelo propuesto, basado en un algoritmo superaprendiz de una y dos etapas, obtuvo el mejor desempeño global, seguido por los métodos de Stacking y Blending, alcanzando valores promedio de precisión del 85 % en entrenamiento y del 75 % en prueba.
El estudio aporta evidencia original al integrar fuentes heterogéneas de datos académicos, administrativos y socioeconómicos con análisis estadístico y técnicas avanzadas de aprendizaje automático, para la construcción de modelos predictivos ajustados al contexto de la educación superior colombiana. No obstante, se identificaron limitaciones asociadas a la disponibilidad y calidad de los datos históricos, así como a la ausencia de variables contextuales no capturadas por los sistemas de información institucionales, lo que puede restringir la generalización de los resultados a otros entornos educativos.
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