Análisis de estrategias innovadoras para retención estudiantil con inteligencia artificial: una perspectiva multidisciplinaria
DOI:
https://doi.org/10.31637/epsir-2024-440Palabras clave:
educación superior, inteligencia artificial, machine learning, redes neuronales, big data, learning analytics, retención estudiantil, revisión sistemáticaResumen
Introducción: La educación superior está transformándose con la adopción de modalidades virtuales e integración de tecnologías como la inteligencia artificial (IA), machine learning (ML), redes neuronales (NN) y big data (BD). Estas tecnologías están redefiniendo el acceso y la retención estudiantil, ofreciendo soluciones personalizadas para mejorar la experiencia educativa en entornos virtuales. Metodología: Esta revisión sistemática, basada en el método PRISMA, examina cómo la interacción de IA, ML, NN y BD influye en la predicción y gestión de la deserción estudiantil, destacando las aplicaciones de learning analytics (LA) para mejorar las intervenciones educativas. Resultados: Los resultados muestran que IA, ML y BD son efectivas para prever y gestionar el abandono escolar, permitiendo intervenciones más personalizadas. El análisis de grandes volúmenes de datos ayuda a identificar patrones cruciales para diseñar estrategias de retención. Discusión: A pesar de las mejoras significativas en la personalización del aprendizaje y optimización de recursos que ofrecen estas tecnologías, enfrentan desafíos éticos y operativos que deben considerarse. Conclusiones: La integración de IA, ML, NN y BD en la educación superior es un enfoque prometedor para enriquecer la experiencia y resultados estudiantiles, destacándose la importancia de inversiones estratégicas y un marco ético robusto para su implementación efectiva.
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