Publication: Big-data en educación V: mapa de riesgos y monitorización automática del abandono académico
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2022
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Abstract
El proyecto avanza en la identificación de las variables digitales válidas para predecir la deserción universitaria en sus distintas áreas de conocimiento. También avanza en un esquema de diagnóstico individualizado para las distintas fases de la deserción académica, que posteriormente puede ser convertido en lenguaje de máquina para su uso por parte de los profesores, para identificar situaciones de riesgo de abandono y realizar intervenciones para evitarlo.
The project advances in the identification of valid digital variables to predict university dropout in its different areas of knowledge. It also advances in an individualised diagnostic scheme for the different phases of academic dropout, which can subsequently be converted into machine language for use by teachers to identify situations of dropout risk and carry out interventions to prevent it.
The project advances in the identification of valid digital variables to predict university dropout in its different areas of knowledge. It also advances in an individualised diagnostic scheme for the different phases of academic dropout, which can subsequently be converted into machine language for use by teachers to identify situations of dropout risk and carry out interventions to prevent it.