Technology Rain Journal

Modelo Computacional de Aprendizaje Adaptativo Basado en Machine Learning para la Optimización de Procesos de Enseñanza en Entornos Digitales

Arturo González Torres

Instituto Tecnológico de Milpa Alta, México.

https://orcid.org/0000-0002-3337-7600

Marina Imelda Terrazas Gómez

Universidad Autónoma de Chihuahua, Delicias, Chihuahua, México.

https://orcid.org/0000-0002-6559-4052

Jordy Christian Granda Feijoo

Universidad Nacional de Loja, Grupo Investigación InnoTEFL, Loja, Ecuador.

https://orcid.org/0000-0001-6767-6409

Vinicio Alexander Chávez Vaca

Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, Calceta, Manabí, Ecuador.

https://orcid.org/0000-0003-3623-4178

DOI: https://doi.org/10.55204/trj.v5i1.e138

Palabras clave: Machine learning, inteligencia artificial, analítica del aprendizaje, minería de datos educativos, personalización del aprendizaje, procesos de enseñanza.

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Resumen

El presente artículo analiza el modelo de aprendizaje adaptativo basado en machine learning como estrategia para optimizar los procesos de enseñanza en entornos educativos mediados por tecnología. A través de una revisión bibliográfica sistemática bajo el enfoque PRISMA, se examinaron estudios recientes relacionados con inteligencia artificial educativa, analítica del aprendizaje, minería de datos educativos, sistemas de recomendación y personalización del aprendizaje. Los resultados evidencian que los algoritmos de machine learning permiten predecir el rendimiento académico, detectar estudiantes en riesgo, construir perfiles dinámicos y recomendar recursos, actividades o rutas formativas ajustadas a las necesidades individuales. Asimismo, se identifica que la efectividad de estos modelos depende de la calidad de los datos, la explicabilidad algorítmica, la privacidad, la interoperabilidad tecnológica y la participación del docente. Se concluye que el aprendizaje adaptativo debe entenderse como un ecosistema sociotécnico que integra datos, algoritmos, pedagogía y acompañamiento humano para favorecer una enseñanza más personalizada, oportuna, ética y centrada en el estudiante, con impacto potencial en la mejora continua de la calidad educativa institucional actual.

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