Predicción del rendimiento académico utilizando modelos de aprendizaje automático: Una revisión sistemática de la literatura
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La predicción del rendimiento académico se ha convertido en un área de creciente interés en la educación superior, debido a su potencial para identificar y apoyar a estudiantes en riesgo antes de que enfrenten dificultades académicas. Este estudio se centra en la aplicación de modelos de aprendizaje automático para predecir el rendimiento académico, explorando diferentes variables y técnicas utilizadas en investigaciones recientes. A través de una revisión sistemática de la literatura, se analizaron estudios que emplean ML para predecir el éxito académico, identificando las variables, criterios, técnicas y las metodologías más efectivas. Los resultados destacan el impacto de variables como el historial académico, factores sociodemográficos, económicos y culturales en el rendimiento estudiantil, así como la eficacia de técnicas como las redes neuronales artificiales, los árboles de decisión y las máquinas de vectores de soporte. Finalmente, se discuten las implicaciones de estos hallazgos para el desarrollo de intervenciones educativas más eficientes y personalizadas.
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Ronaldo Andre Del Carpio-Mendoza, Universidad Nacional Mayor de San Marcos - Perú
https://orcid.org/0009-0006-6854-038X
Licenciado en Matemáticas, Universidad Nacional de San Agustín, Maestrando en ingeniería de sistemas e informática con mención en ingeniería de software, Universidad Nacional Mayor de San Marcos. Docente de la Universidad Católica San Pablo.
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