Predicción de la resiliencia docente mediante redes neuronales artificiales: influencia del burnout y del estrés por COVID-19

Autores/as

DOI: https://doi.org/10.6018/analesps.515611
Palabras clave: COVID-19, Estrés, Inteligencia artificial, profesorado, Resiliencia, Síndrome de estar quemado

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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Biografía del autor/a

Juan Pedro Martínez-Ramón, Universidad de Murcia

Doctor y licenciado en Psicología. 

Licenciado en Psicopedagogía.

Profesor asociado en el Dpto. de Psicología Evolutiva y de la Educación.

Funcionario de carrera del cuerpo de profesores de enseñanza secundaria, especialidad orientación educativa, de la Consejeria de Educación de la Región de Murcia.

Representante de la vocalía de Psicología Educativa y vocal de la Junta de Gobierno de Colegio Oficial de Psicólogos de la Región de Murcia.

 

Francisco Manuel Morales-Rodríguez, Universidad de Granada

Profesor en el Departamento de Psicología Evolutiva y de la Educación de la Universidad de Granada.

Sergio Pérez-López, Universidad de Murcia

Graduado en Pedagogía por la Universidad de Murcia.

Inmaculada Méndez Mateo, Universidad de Murcia

Profesor titular del Departamento de Psicología Evolutiva y de la Educación de la Universidad de Murcia. Vicedecana de Calidad de la Facultad de Educación.

Cecilia Ruiz-Esteban, Universidad de Murcia

Profesor titular del Departamento de Psicología Evolutiva y de la Educación de la Universidad de Murcia.

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Publicado
01-01-2023
Cómo citar
Martínez-Ramón, J. P., Morales-Rodríguez, F. M., Pérez-López, S., Méndez Mateo, I., & Ruiz-Esteban, C. (2023). Predicción de la resiliencia docente mediante redes neuronales artificiales: influencia del burnout y del estrés por COVID-19. Anales de Psicología / Annals of Psychology, 39(1), 100–111. https://doi.org/10.6018/analesps.515611
Número
Sección
Psicología evolutiva y de la educación

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