Prediciendo el rendimiento académico de estudiantes de pregrado en una universidad destacada de Perú: Una aproximación con herramientas de Machine Learning
Resumen
Aunque la accesibilidad a la educación superior ha mejorado en países de renta baja y media (PRMB), persiste el abandono, especialmente entre estudiantes socioeconómicamente desfavorecidos. A pesar de los avances en modelos de Machine Learning para entender este desafío, muchos estudios descuidan factores institucionales específicos de los PRMB o se centran en cursos específicos, limitando su aplicabilidad y relevancia política. Para abordar esto, creamos una base de datos usando registros administrativos y censales para predecir el rendimiento académico en la Pontificia Universidad Católica del Perú (PUCP). Los modelos más efectivos, entre ellos Random Forest, destacaron predictores como el rendimiento previo y puntuaciones en pruebas de admisión. Presentamos un modelo eficiente con diez características que puede predecir el rendimiento futuro y así aportar a la reducción de la deserción en PUCP.
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