Burnout, Resilience, and COVID-19 among Teachers: Predictive Capacity of an Artificial Neural Network

Abstract

Emotional exhaustion, cynicism, and work inefficiency are three dimensions that define burnout syndrome among teachers. On another note, resilience can be understood as the ability to adapt to the environment and overcome adverse situations. In addition, COVID-19 has provided a threatening environment that has led to the implementation of resilience strategies to struggle with burnout and cope with the virus. The aim of this study was to analyze the relationship between resilience, burnout dimensions, and variables associated with COVID-19 through the design of an artificial neural network architecture. For this purpose, the Maslach Burnout Inventory-General Survey (MBI-GS), the Brief Resilience Coping Scale (BRCS), and a questionnaire on stress towards COVID-19 were administered to 419 teachers from secondary schools in southeastern Spain (292 females; 69.7%). The results showed that 30.8% suffered from burnout (high emotional exhaustion, high cynicism, and low professional efficacy) and that 38.7% had a high level of resilience, with an inverse relationship between both constructs. Likewise, we modelled an ANN able to predict burnout syndrome among 97.4% of teachers based on its dimensions, resilience, sociodemographic variables, and the stress generated by COVID-19. Our conclusions shed some light on the efficacy of relying on artificial intelligence in the educational field to predict the psychological situation of teachers and take early action.

Psychosocial status of Physical Education teachers according to socio-demographic characteristics (Condición psicosocial de los profesores de Educación Física según las características sociodemográficas)

“…Very distant results were obtained by de Vera and Gambarte (2019), stating de Vera-García and Gabari-Gambarte, (2019) that resilience is a protective factor against disruptive states such as burnout syndrome and stress. In this case, the study by Martínez-Ramón et al (2021) states that, in the presence of adverse situations, teachers with higher levels of resilience do not burn out, but acquire a greater work commitment, overcoming difficulties. Likewise, for H.3, positive relationships are observed with emotional exhaustion and depersonalisation, however, a negative relationship is obtained with personal fulfilment.…”

Influence of Resilience on Burnout Syndrome of Faculty Professors

“…As a result, the word “resilience” refers to the protective features that humans build through time. In other words, this construct is not a concept that is inherited and passed down from generation to generation, but rather a term that is acquired and developed throughout life and via adverse experiences and emotional control that individuals have over themselves [ 62 ].…”

Self-Esteem at University: Proposal of an Artificial Neural Network Based on Resilience, Stress, and Sociodemographic Variables

“…Precisely to study the complex relationship between variables, artificial intelligence (AI), including artificial neural networks, is a growing technology with great implications in the field of education ( Martínez-Ramón et al, 2021 ). In particular, AI is able to reduce human error and perform predictive analytics with less data than traditional systems, and the education system is expected to benefit from the technology surrounding this technology through more realistic decision-making processes ( Colchester et al, 2016 ).…”

“…Today, the specialization of AI in the educational field is leading to the concept of IAEd, which is helping teachers to simulate hypothetical scenarios and manage a large amount of data related to the teaching-learning process, school coexistence, attention to diversity, among other aspects ( Schiff, 2021 ). This is even more critical in the wake of the pandemic resulting from COVID-19 ( Martínez-Ramón et al, 2021 ), and the need to create online learning environments and to operate with a large amount of information ( Colchester et al, 2016 ). Because of the novelty and multiple applications of AI and big data, it is necessary to promote studies that make use of this technology and to train the educational community in the development of applications, in the improvement of research and even, ultimately, in decision making on issues related to educational policies ( Colchester et al, 2016 ).…”

“…Because of the novelty and multiple applications of AI and big data, it is necessary to promote studies that make use of this technology and to train the educational community in the development of applications, in the improvement of research and even, ultimately, in decision making on issues related to educational policies ( Colchester et al, 2016 ). In addition to what has been said so far, sociodemographic variables such as age or gender can contribute to some extent to predict the cognitive processes of teachers, although their impact on artificial neural networks still requires further study ( Martínez-Ramón et al, 2021 ; Morales-Rodríguez et al, 2021 ). Without a shadow of doubt, it is a problem that needs addressing.…”