What If Violent Behavior Was a Coping Strategy? Approaching a Model Based on Artificial Neural Networks

Abstract

The aggressor sets in motion dysfunctional and violent behaviors with others in the dynamic of bullying. These behaviors can be understood as misfit coping strategies in response to environmental demands perceived as stressful, putting at risk the quality of education. The aim of this study was to develop a predictive model based on artificial neural networks (ANN) to forecast a violent coping strategy based on perceived stress, resilience, other coping strategies and various socio-demographic variables. For this purpose, the Stress Coping Questionnaire (SCQ), the Perceived Stress Scale (PSS) and the Brief Resilient Coping Scale (BRCS) were administered to 283 participants from the educational field (71.5% women). The design was cross-sectional. An inferential analysis (multilayer perception ANN) was performed with SPSS version 24. The results showed a predictive model that took into consideration the subject’s stress levels, personal assessment and strategies such as negative self-targeting or avoidance to predict open emotional expression (a coping strategy defined by violent behaviors) in approximately four out of five cases. The conclusions emphasis the need for considering problem solving, stress management and coping skills to prevent school violence and improve the social environment through sustainable psychological measures.

Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment

“…Example of an indirect item: (item 9) “In the last month, how often have you been able to control the difficulties in your life?”. In the Spanish version, a Cronbach’s alpha of 0.81 was obtained ( Remor, 2006 ), and in a recent research it was found to be 0.86 ( Martínez and Morales, 2020 ). In this study, it was 0.857.…”

“…Coping strategies are often associated with the need to combat a perceived threatening situation ( Martínez and Morales, 2020 ; Martínez et al, 2020 ). In order to cope with stress during confinement, seeking social support was found to be one of the most used strategies among university students ( Lechner et al, 2020 ).…”

“…Similar to coping strategies, artificial neural networks (ANNs) have also proven to be highly effective in developing frameworks adjusted to reality with the capacity to predict variables present in the university community members. Since this is a relatively recent form of data processing, ANNs potentially require further development in the field of education, and there is a clear need for additional research on this subject ( Martínez and Morales, 2020 ). In relation to the theoretical model and in parallel to what has been explained thus far, educational psychology, as a branch of knowledge that is concerned with the variables affecting the teaching and learning processes of individuals, is ideal for facing new challenges, shedding light on the topic, favoring innovation, and analyzing the phenomenon under investigation using a scientific method ( de la Fuente et al, 2018a ).…”

“…Therefore, as has been explained, the university is an environment in which people coexist and, due to various circumstances, they present high levels of stress ( Amaral-Prado et al, 2020 ). These people implement a series of coping strategies to manage stress ( Martínez and Morales, 2020 ). The questions that emerge are diverse: Does stress really exist in people related to the university?…”

“…(1) to analyze the variable of stress and its relationship with coping strategies, and resilience; (2) to examine the differences between the pre-covid and post-covid period on stress, resilience, and coping strategies; and (3) to design and select the artificial neural network that has the best predictive capability. These objectives are based on the fact that relationships between stress and the use of certain dysfunctional coping strategies have been found in the scientific literature ( Oliva et al, 2018 ; Martínez and Morales, 2020 ) as well as between stress and low resilience before ( Keech et al, 2018 ) and after COVID-19 ( Liu C. H. et al, 2020 ). The complexity of the variables involved in predicting human behavior in confinement justifies the use of artificial intelligence techniques to shed light on the issue ( Zawacki-Richter et al, 2019 ).…”