Algoritmos de aprendizaje automático para predecir la efectividad atribuida a la educación penitenciaria por estudiantes universitarios
Resumen
La educación penitenciaria constituye un eje estratégico en los procesos de reinserción social y garantía del derecho a la educación en contextos de privación de libertad. El objetivo del trabajo fue predecir la efectividad de la educación penitenciaria desde la perspectiva de estudiantes de la carrera de Derecho en la Universidad Estatal de Milagro, Ecuador. La metodología adoptó un enfoque cuantitativo, de tipo aplicada, con diseño no experimental y corte transversal. La muestra intencional estuvo conformada por 100 estudiantes, a los que se les aplicó un cuestionario estructurado validado por expertos. Los datos fueron procesados mediante análisis multivariado y técnicas de minería de datos en Orange Data Mining, utilizando modelos como AdaBoost, regresión logística y gradient boosting. Se evidenció que las variables con mayor capacidad explicativa fueron el diseño e implementación de programas educativos y el impacto en la resocialización; mientras que las variables sociodemográficas mostraron escasa incidencia predictiva. El modelo AdaBoost obtuvo el mayor rendimiento en métricas de precisión, sensibilidad y calibración. Se concluye que la percepción estudiantil sobre la efectividad de la educación penitenciaria está determinada más por factores pedagógicos que biográficos, y que los modelos predictivos constituyen una herramienta robusta para fortalecer la formación en derechos humanos.
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