Predicción de la resiliencia docente mediante redes neuronales artificiales: influencia del burnout y del estrés por COVID-19
Resumen
Antecedentes: La resiliencia en el profesorado permite afrontar situaciones difíciles y reponerse a la adversidad existiendo diferencias de género al respecto. Asimismo, la inteligencia artificial y las técnicas asociadas a ella han resultado ser de gran utilidad para predecir variables educativas y estudiar la interconexión entre ellas tras la COVID-19. Dicho esto, el objetivo general de esta investigación fue predecir los niveles de resiliencia en las profesoras y profesores de Secundaria a través del diseño de una red neuronal artificial (RNA). Método: Se administró la Escala Breve de Afrontamiento Resiliente, el Inventario de Burnout de Maslach y el Cuestionario de Estrés frente a la COVID-19 a 401 docentes de secundaria (70.6% mujeres) de centros educativos del sureste español, con una edad media de 44.36 años (DT= 9.38). Resultados: Se hallaron diferencias en la configuración de los modelos predictivos de la resiliencia entre profesoras y profesores contribuyendo las variables independientes en diferente grado en función del género. Conclusiones: Se pone de manifiesto la utilidad de las RNA en el ámbito educativo y la necesidad de diseñar programas más ajustados.
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