Modelos de analítica predictiva para la gestión del rendimiento académico en estudiantes de posgrado en modalidad virtual
DOI:
https://doi.org/10.5281/zenodo.18274074Palavras-chave:
aprendizaje automático, educación superior digital, deserción académica, educación en línea, tecnología educativa, intervención pedagógica, minería de datosResumo
Evaluar la eficacia de los modelos de analítica predictiva en la identificar factores vinculados al rendimiento académico en alumnos de maestría virtual de universidades privadas de Lima Metropolitana. El estudio efectuó un enfoque de tipo cuantitativo, de diseño correlacional-predictivo. Teniendo por muestra de 120 alumnos de programas de posgrado de manera virtual en universidades privadas en el período académico 2024-2025. Asimismo, para valorar la relación de las variables académicas, sociodemográficas y modelos de cooperación en plataformas digitales se efectuaron técnicas de regresión logística y árboles de decisión. El instrumento usado se consiguió a través de cuestionarios organizados y validados por análisis sistemático de registros institucionales. El modelo de analítica predictiva efectuado alcanzó una precisión 79.2% de precisión en la clasificación interna, lo que sugiere viabilidad técnica en este contexto. Las variables que mostraron mayor nivel de significancia estadística fueron: tiempo dedicado al estudio semanal (β=0.49, p<0.01), participación activa en actividades simultáneas (β=0.56, p<0.01), y experiencia previa en modelos de educación virtual (β=0.45, p<0.05). La implementación táctica de modelos predictivos consiente a las instituciones diseñar e implementar intervenciones formativas focalizadas y oportunas.
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