Machine Learning aplicado al rendimiento académico en educación superior: factores, variables y herramientas
Sinopsis
Las herramientas de aprendizaje automático están siendo muy utilizadas por sus buenas aproximaciones al predecir el rendimiento académico de los estudiantes. Se analiza información de la última década con el objetivo de identificar los factores que influyen sobre el rendimiento académico en el nivel superior, a partir de modelos realizados por medio de técnicas de aprendizaje automático. Se plantea una clasificación en factores académicos, sociodemográficos, de aprendizaje en línea, de gestión académica, psicosocial y de entorno académico. También se identifican los algoritmos más usados en su predicción.
Adicionalmente, la detección de las variables que más influyen en el fenómeno permitirá implementar algoritmos de Machine Learning pertenecientes a otras ramas de este campo. Así pues, al ahondar un poco más en la aplicación de herramientas de Machine Learning en educación superior, este trabajo servirá a docentes e investigadores que deseen investigar estos temas.
Capítulos
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Capítulo 1Contextualización del tema de investigación y su desarrollo
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Capítulo 2Rendimiento académico
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Capítulo 3Machine Learning
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Capítulo 4Aprendizaje no supervisado: factores y variables que influyen en el rendimiento académico
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Capítulo 5Aprendizaje no supervisado: investigación referencial
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Capítulo 6Aprendizaje no supervisado: algoritmos implementados
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Capítulo 7Aprendizaje supervisado: Factores y variables que influyen en el rendimiento académico
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Capítulo 8Aprendizaje supervisado: Investigación referencial
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Capítulo 9Aprendizaje supervisado: Algoritmos implementados
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Capítulo 10Redes Neuronales: factores y variables que influyen en el rendimiento académico
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Capítulo 11Redes neuronales: Investigación referencial
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Capítulo 12Redes neuronales: algoritmos implementados
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Capítulo 13Análisis de software usados/relacionados con Machine Learning aplicado al rendimiento académico en educación superior
Descargas
Referencias
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