Predicción de rendimiento del curso en línea en la educación superior con datos de LMS: Una revisión sistemática de literatura
DOI:
https://doi.org/10.47230/unesum-ciencias.v9.n2.2025.202-219Palabras clave:
Minería de datos educativos (EDM), LMS, predicción, rendimiento, educación superiorResumen
Las instituciones de educación superior han incrementado el uso de sistemas de gestión de aprendizaje (LMS) para facilitar la educación en línea y optimizar los procesos formativos. Estos entornos no solo permiten el acceso remoto a contenidos y actividades, sino que también generan grandes volúmenes de datos sobre las interacciones entre estudiantes y docentes, los cuales pueden utilizarse para predecir el rendimiento en cursos en línea. Con la expansión de la educación virtual, se reconoce la necesidad de métodos precisos de predicción basados en datos registrados por los LMS. En este marco, la minería de datos educativa (EDM) se plantea como una herramienta eficaz para identificar patrones y apoyar la toma de decisiones académicas. Este artículo presenta una revisión sistemática de la literatura sobre el uso de datos de LMS para la predicción del rendimiento académico en línea, considerando técnicas y enfoques aplicados. Las técnicas más utilizadas incluyen regresión logística, árboles de decisión, redes bayesianas y redes neuronales, aplicadas al análisis de datos relacionados con accesos, calificaciones y participación. Asimismo, se identifican como métricas comunes de evaluación la exactitud, precisión y el error promedio, y como procesos clave la selección de características, normalización y validación cruzada. Este estudio proporciona una visión actualizada del uso de técnicas de aprendizaje automático en entornos virtuales, contribuyendo a futuras investigaciones sobre el análisis del rendimiento estudiantil en la educación superior en línea.
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