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.

Citas

Abdul Aziz, I., Tajularipin, S., & Samsilah R. (2020). Models of Relationship between Emotional, Spiritual, Physical and Social Intelligence, Resilience and Burnout among High School Teachers. Universal Journal of Educational Research, 8(1A), 1-7. https://doi.org/10.13189/ujer.2020.081301.

Ainsworth, S., & Oldfield, J. (2019). Quantifying teacher resilience: Context matters. Teaching and Teacher Education. 82, 117-128. https://doi.org/10.1016/j.tate.2019.03.012

Altuntaş, S., & Genç, H. (2020). Resilience as predictor of happiness: Investigation of teacher sample. Hacettepe University Journal of Education, 35(4), 936-948. https://doi.org/10.16986/HUJE.2018046021

Arias, W. L., Huamani, J., & Ceballos, K. D. (2019). Síndrome de Burnout en profesores de escuela y universidad: un análisis psicométrico y comparativo en la ciudad de Arequipa [Burnout syndrome in school and university teachers: a psychometric and comparative analysis in the city of Arequipa]. Propósitos y Representaciones, 7(3), 72-110. http://dx.doi.org/10.20511/pyr2019.v7n3.390

Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., & Karadeniz, A. (2020). An Early Warning System to Detect At-Risk Students in Online Higher Education. Applied Sciences, 10(13), 4427. http://dx.doi.org/10.3390/app10134427

Benvenuto, G., Di Genova, N., Nuzzaci, A., & Vaccarelli, A. (2021). Scala di Resilienza Professionale degli Insegnanti: prima validazione nazionale. Journal of educational , cultural and psychological studies, 23, 201-218. https://dx.doi.org/10.7358/ecps-2021-023-benv

Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800

Buddhtha, S., Natasha, C., Irwansyah. E., & Budiharto, W. (2019). Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS. International Journal of Computational Intelligence Systems, 12(2), 1575-1584.https://doi.org/10.2991/ijcis.d.191101.003

Candeias, A., Galindo, E., Calisto, I., Borralho, L., & Reschke, K. (2021). Stress and burnout in teaching. Study in an inclusive school workplace. Health Psychology Report, 9(1), 63-75. https://doi.org/10.5114/hpr.2020.100786

Carlotto, M. S., y Câmara, S. G. (2017). Burnout syndrome profiles among teachers. Escritos de Psicología-Psychological Writings, 10(3), 159-166. https://doi.org/10.5231/psy.writ.2017.2911

Carmona, J.F., & Muñoz, C.F. (2021). Diagnosis and Intervention Program for Burnout Syndrome in Primary and Secondary Teachers at a School in Pereira. Universal Journal of Public Health, 9(2), 75 - 82. http://dx.doi.org/10.13189/ujph.2021.090206.

Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 100005.https://doi.org/10.1016/j.caeai.2020.100005

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G.(2016).A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1) 47-64. https://doi.org/10.1515/jaiscr-2017-0004

Diat Prasojo, L., Habibi. A., Faiz Mohd Yaakob, M., Pratama, R, Rahimi Yusof, M., Mukminin, A., Suyanto y Hanum, F. (2020). Teachers’ burnout: A SEM analysis in an Asian context. Heliyon, 6(1), 3144. https://doi.org/10.1016/j.heliyon.2019.e03144

Dignum, V. (2021) ‘The role and challenges of education for responsible AI’. London Review of Education, 19(1), 1–11.https://doi.org/10.14324/LRE.19.1.01

Goksel, N., & Bozkurt, A. (2019). Artificial intelligence in education: Current insights and future perspectives. In Handbook of Research on Learning in the Age of Transhumanism (pp. 224-236). Pensilvania (Estados Unidos): IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch014

Guan, M. (2020). Mental burnout of english teachers and countermeasures. Revista Argentina de Clínica Psicológica, 29(2), 244. https://doi.org/10.24205/03276716.2020.231

Guo, J., Bai, L., Yu, Z., Zhao, Z., & Wan, B. (2021). An AI-ApplicationOriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning. Sensors, 21(1), 241. https://doi.org/10.3390/s21010241

Hlaďo, P., Dosedlová, J., Harvánková, K., Novotný, P., Gottfried, J., Rečka, K., Petrovová, M., et al. (2020). Work Ability among Upper-Secondary School Teachers: Examining the Role of Burnout, Sense of Coherence, and Work-Related and Lifestyle Factors. International Journal of Environmental Research and Public Health, 17(24), 9185. http://dx.doi.org/10.3390/ijerph17249185

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001

Karakus, M., Ersozlu, Z., Usak, M., & Ocean, J. (2021). Self-efficacy, affective well-being, and intent-to-leave by science and mathematics teachers: A structural equation model. Journal of Baltic Science Education, 20(2),237-251. https://doi.org/10.33225/jbse/21.20.237

Kim, L.E., Jörg, V., & Klassen, R.M. (2019). A Meta-Analysis of the Effects of Teacher Personality on Teacher Effectiveness and Burnout. Educational Psycholy Review, 31, 163–195. https://doi.org/10.1007/s10648-018-9458-2

Kroupis, I., Kourtessis, T., Kouli, O., Tzetzis, G., Derri, V., & Mavrommatis, G. (2017). Job satisfaction and burnout among Greek PE teachers. A comparison of educational sectors, level and gender.(Satisfacción laboral y burnout de los profesores de la educación física en Grecia). Cultura, ciencia y deporte, 12(34), 5-14. https://doi.org/10.12800/ccd.v12i34.827

Kroupis, I., Kourtessis, T., Kouli, O., Tzetzis, G., Derri, V., & Mavrommatis, G. (2017). Job satisfaction and burnout among Greek P.E. teachers. A comparison of educational sectors, level and gender. (Satisfacción laboral y burnout de los profesores de la educación física en Grecia) [A comparison of educational sectors, level and gender (Job satisfaction and burnout of physical education teachers in Greece)]. Cultura, Ciencia y Deporte, 12(34), 5-14. https://doi.org/10.12800/ccd.v12i34.827

Liu, F., Chen, H., Xu, J., Wen, Y., & Fang, T. (2021). Exploring the Relationships between Resilience and Turnover Intention in Chinese High School Teachers: Considering the Moderating Role of Job Burnout. International Journal of Environmental Research and Public Health, 18(12), 6418. https://doi.org/10.3390/ijerph18126418

Llorca-Pellicer, M., Soto-Rubio, A., & Gil-Monte, P. R. (2021) Development of Burnout Syndrome in Non-university Teachers: Influence of Demand and Resource Variables. Frontiers in Psychology, 12, 644025. https://doi.org/10.3389/fpsyg.2021.644025

Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai C. C. (2020) Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820

Marić, N., Mandić-Rajčević, S., Maksimović, N., y Bulat, P. (2020). Factors Associated with Burnout Syndrome in Primary and Secondary School Teachers in the Republic of Srpska (Bosnia and Herzegovina). International Journal of Environmental Research and Public Health, 17(10), 3595. MDPI AG. http://dx.doi.org/10.3390/ijerph17103595

Autor (2021).

Maslach, C., & Jackson, S.E. (1986). Maslach Burnout Inventory. Palo alto, CA: Consulting Psychologists Press.

Matiz, A., Fabbro, F., Paschetto, A., Cantone, D., Paolone, A. R., & Crescentini, C. (2020). Positive Impact of Mindfulness Meditation on Mental Health of Female Teachers during the COVID-19 Outbreak in Italy. International Journal of Environmental Research and Public Health, 17(18), 6450. http://dx.doi.org/10.3390/ijerph17186450

Moret-Tatay, C., Fernández-Muñoz, J.J., Civera-Mollá, C., Navarro-Pardo, E., & Alcover-de-la-Hera, C. (2015). Propiedades psicométricas y estructura factorial del BRCS en una muestra de personas mayores españolas [Psychometric properties and factorial structure of the BRCS in a sample of Spanish elderly people]. Anales de Psicología, 31, 1030–1034.

Mota, A. I., Lopes, J., & Oliveira, C. 2021. Teachers Voices: A Qualitative Study on Burnout in the Portuguese Educational System. Education Sciences, 11(8), 392. https://doi.org/10.3390/educsci11080392

Paek, S., & Kim, N. (2021). Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education. Sustainability, 13(14), 7941. https://doi.org/10.3390/su13147941

Platsidou, M., y Daniilidou, A. (2021). Meaning in life and resilience among teachers. Journal of Positive School Psychology, 5(2), 97–109. https://doi.org/10.47602/jpsp.v5i2.259

Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3) 135-153. https://doi.org/10.2478/jolace-2019-0025

Polat, D. D., y İskender, M. (2018). Exploring teachers’ resilience in relation to job satisfaction, burnout, organizational commitment and perception of organizational climate. International Journal of Psychology and Educational Studies, 5(3), 1-13.https://doi.org/10.17220/ijpes.2018.03.001

Reiss, M. J. (2021). The use of AI in education: Practicalities and ethical considerations. London Review of Education, 19(1), 1–14. https://doi.org/10.14324/LRE.19.1.05

Roohani, A., & Iravani, M. (2020). The Relationship Between Burnout and Self-Efficacy among Iranian Male and Female EFL Teachers. Journal of Language and Education, 6(1), 173-188. https://doi.org/10.17323/jle.2020.9793

Salanova, M., Schaufeli, W. B., Llorens, S., Peiró, J. M., & Grau, R. (2000). Desde el "burnout" al "engagement": ¿una nueva perspectiva? [From burnout to engagement: a new perspective?] Revista de Psicología del Trabajo y las Organizaciones, 16(2), 117-134.

Salmela-Aro, K., Hietajärvi, L., & Lonka, K. (2019) Work Burnout and Engagement Profiles Among Teachers. Frontiers in Psychology, 10, 2254. http://dx.doi.org/10.3389/fpsyg.2019.02254

Sánchez-Pujalte, L., Navarro, M., Etchezahar, E., y Gómez, T. (2021). Teachers’ Burnout during COVID-19 Pandemic in Spain: Trait Emotional Intelligence and Socioemotional Competencies. Sustainability, 13(13), 7259. https://doi.org/10.3390/su13137259

Schiff, D. (2021). Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI & Society, 36, 331–348. https://doi.org/10.1007/s00146-020-01033-8

Seibt, R., & Kreuzfeld, S. (2021). Influence of Work-Related and Personal Characteristics on the Burnout Risk among Full- and Part-Time Teachers. International Journal of Environmental Research and Public Health, 18(4), 1535. http://dx.doi.org/10.3390/ijerph18041535

Sinclair, V. G., & Wallston, K. A. (2004). The development and psychometric evaluation of the Brief Resilient Coping Scale. Assessment, 11, 94–101.

Szempruch, J. (2018). Feeling of Professional Burnout in Teachers of Secondary Schools. The New Educational Review, 54, 219-230. https://doi.org/10.15804/tner.2018.54.4.18

Tsang, K. K., Teng, Y., Lian, Y., y Wang, L. (2021). School Management Culture, Emotional Labor, and Teacher Burnout in Mainland China. Sustainability, 13(16), 9141. http://dx.doi.org/10.3390/su13169141

Väisänen, S., Pietarinen, J., Pyhältö, K., Toom, A., & Soini, T. (2018). Student teachers’ proactive strategies for avoiding study-related burnout during teacher education. European Journal of Teacher Education, 41(3), 301-317. https://doi.org/10.1080/02619768.2018.1448777

Valentino, S., & Sosnowski, C. (2019). Emerging theory of teacher resilience: a situational analysis. English Teaching: Practice & Critique, 18(4), 492-507. https://doi.org/10.1108/ETPC-12-2018-0118

Valosek, L., Wendt, S., Link, J., Abrams, A., Hipps, J., Grant, J., Nidich, R., Loiselle, M., & Nidich, S. (2021). Meditation Effective in Reducing Teacher Burnout and Improving Resilience: A Randomized Controlled Study. Frontiers in Education, 6, 627923. http://dx.doi.org/10.3389/feduc.2021.627923

Vicente de Vera, M. I., & Gabari Gambarte, M. I. (2019). Burnout y Factores de Resiliencia en Docentes de Educación Secundaria [Burnout and Resilience Factors in Secondary School Teachers]. International Journal of Sociology of Education, 8(2), 127-152. https://doi.org/10.17583/rise.2019.3987

Yada, A., Bjorn, M. P., Savolainen, P., Kyttala, M., Aro, M., & Savolainen, H. (2021). Pre service teachers’self-efficacy in implementing inclusive practicesand resilience in Finland. Teaching and Teacher Education, 105, 103398. https://doi.org/10.1016/j.tate.2021.103398

Yang, C. (2019). Correlation between mental health, work pressure and job burnout of music teachers. Revista Argentina de Psicología Clínica, 29(2), 542. http://dx.doi.org/10.24205/03276716.2020.275

Yorulmaz, Y. İ., & Altinkurt, Y. (2018). The examination of teacher burnout in Turkey: A meta-analysis. Turkish Journal of Education, 7(1), 34-54. https://doi.org/10.19128/turje.348273

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
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Psicología evolutiva y de la educación

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