Prediciendo el abandono universitario utilizando técnicas de aprendizaje automático: Aplicaciones sobre bases de datos multidisciplinarias
Resumen La deserción estudiantil constituye un desafío para las instituciones de Educación Superior debido, entre otras razones, a la diversidad de los estudiantes en cuanto a su origen social, educativo y económico, así como a las disciplinas y grados académicos en los que se matriculan, produciendo una amplia variabilidad en las trayectorias educativas. El objetivo de este estudio fue desarrollar un modelo predictivo para el abandono universitario en estudiantes pertenecientes a diferentes titulaciones, basado en machine learning. Se utilizaron un cuestionario online y los expedientes académicos del estudiantado para obtener diez variables previamente identificadas como relevantes para el fenómeno estudiado y se entrenaron clasificadores mediante procedimientos de aprendizaje automático supervisado. La muestra incluyó 946 estudiantes pertenecientes a siete áreas de conocimiento en una universidad de Chile. Los principales resultados indican que un modelo basado en el clasificador RUSboosted Trees presentó el mejor desempeño para la predicción de la deserción en Educación Superior, reconociendo exitosamente al 90% de las y los estudiantes desertores. Varios estudios previos han enfrentado la tarea de proporcionar herramientas para la predicción de la deserción estudiantil utilizando aprendizaje automático, pero generalmente sobre muestras de estudiantes pertenecientes a las mismas titulaciones o áreas del conocimiento. Este estudio proporciona un modelo predictivo que identifica a los futuros estudiantes desertores, incluso en condiciones de multidisciplinariedad dentro de sus programas de origen.
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