Análisis sobre las plataformas LMS considerando el deep learning y random forest
DOI:
https://doi.org/10.12795/revistafuentes.2024.24123Palabras clave:
Tecnología, Enseñanza, Tecnología de la información, Método de enseñanza, Educación, Aprendizaje, Tecnología educacional, Enseñanza multimediaResumen
Hoy en día, los educadores se apoyan en la tecnología para ofrecer a los estudiantes nuevos ambientes de aprendizaje. El objetivo de este estudio mixto es analizar la percepción de los estudiantes sobre el uso de las plataformas LMS durante la postpandemia COVID-19 por medio los algoritmos deep learning y random forest. Los resultados del algoritmo deep learning señalan que el uso de las plataformas LMS afecta positivamente el análisis y uso de la información escolar, la autonomía y el intercambio de ideas durante el proceso de aprendizaje. Asimismo, el algoritmo random forest permitió la construcción de tres modelos sobre esta herramienta tecnológica considerando el perfil de los estudiantes. Las limitaciones de esta investigación cuantitativa y cualitativa son la muestra y las variables dependientes. Por consiguiente, las futuras investigaciones pueden analizar el uso de las plataformas LMS durante la postpandemia COVID-19 considerando el rol activo de los estudiantes, el desarrollo de habilidades y la asimilación del conocimiento en diversas escuelas, facultades e institutos. Las implicaciones de este estudio están relacionadas con el uso de las plataformas LMS para eliminar las barreras físicas, fomentar el aprendizaje personalizado y actualizar las actividades bajo la modalidad a distancia. En conclusión, los educadores deben de incluir las plataformas LMS en la planeación de los cursos con el propósito de facilitar el proceso de aprendizaje.
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Publicado
2024-05-15
Cómo citar
Salas-Rueda, R.-A. (2024). Análisis sobre las plataformas LMS considerando el deep learning y random forest. Revista Fuentes, 26(2), 134–146. https://doi.org/10.12795/revistafuentes.2024.24123
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Recibido 2023-07-09
Aceptado 2023-11-03
Publicado 2024-05-15
Aceptado 2023-11-03
Publicado 2024-05-15
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