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Modelado predictivo y análisis de datos de cohorte para el éxito y la retención de estudiantes
1Computer Engineering and Computer Science Department, California State University, Long Beach, 90840, CA, USA.
Evaluation and program planning
|September 2, 2025
Resumen
El éxito académico varía según la demografía de los estudiantes. Los estudiantes de minorías y elegibles para Pell enfrentan desafíos, pero los modelos predictivos pueden identificar a los estudiantes en riesgo para un apoyo específico y una mejor retención.
Área de la Ciencia:
- Investigación en educación superior
- Minería de datos educativos
- Análisis del éxito de los estudiantes
Sus antecedentes:
- El rendimiento académico y la retención son métricas críticas en la educación superior.
- Comprender las disparidades demográficas es esencial para un apoyo equitativo a los estudiantes.
- El modelado predictivo ofrece oportunidades para la intervención temprana en el éxito de los estudiantes.
Objetivo del estudio:
- Analizar las disparidades de rendimiento académico entre diversas poblaciones estudiantiles.
- Identificar los factores clave que influyen en los resultados de los estudiantes (GPA, acumulación de créditos).
- Desarrollar y validar modelos predictivos para pronosticar el éxito de los estudiantes.
Principales métodos:
- Un análisis basado en datos de más de 23.000 estudiantes de primer año.
- Examen de los factores: promedio general, acumulación de créditos, elegibilidad para la Beca Pell, condición de minoría, educación de los padres.
- Aplicación del análisis de agrupación y el aprendizaje profundo para el modelado predictivo.
Principales resultados:
- Se observan diferencias significativas: los estudiantes de minorías y elegibles para Pell acumulan menos créditos.
- Se observó un promedio más bajo y una variación más amplia en el promedio para los estudiantes de minorías.
- Se identificaron tres perfiles de participación académica distintos a través del análisis de agrupación.
Conclusiones:
- El rendimiento heterogéneo de los estudiantes requiere estrategias de apoyo diferenciadas.
- Los modelos predictivos predicen con precisión la acumulación de créditos de segundo año y el promedio de calificaciones.
- Los hallazgos proporcionan ideas prácticas para mejorar la retención de estudiantes y el impulso académico.
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