Martínez-Ramón Juan Pedro, Morales-Rodríguez Francisco Manuel, Ruiz-Esteban Cecilia, Méndez Inmaculada
Department of Evolutionary and Educational Psychology, Faculty of Psychology, Campus Regional Excellence Mare Nostrum, University of Murcia, Murcia, Spain.
Department of Evolutionary and Educational Psychology, Faculty of Psychology, Cartuja Campus, University of Granada, Granada, Spain.
Front Psychol. 2022 Feb 28;13:815853. doi: 10.3389/fpsyg.2022.815853. eCollection 2022.
Artificial intelligence (AI) is a useful predictive tool for a wide variety of fields of knowledge. Despite this, the educational field is still an environment that lacks a variety of studies that use this type of predictive tools. In parallel, it is postulated that the levels of self-esteem in the university environment may be related to the strategies implemented to solve problems. For these reasons, the aim of this study was to analyze the levels of self-esteem presented by teaching staff and students at university ( = 290, 73.1% female) and to design an algorithm capable of predicting these levels on the basis of their coping strategies, resilience, and sociodemographic variables. For this purpose, the Rosenberg Self-Esteem Scale (RSES), the Perceived Stress Scale (PSS), and the Brief Resilience Scale were administered. The results showed a relevant role of resilience and stress perceived in predicting participants' self-esteem levels. The findings highlight the usefulness of artificial neural networks for predicting psychological variables in education.
人工智能（AI）是适用于广泛知识领域的一种有用的预测工具。尽管如此，教育领域仍然是一个缺乏使用这类预测工具的各类研究的环境。与此同时，据推测，大学环境中的自尊水平可能与解决问题所采用的策略有关。基于这些原因，本研究的目的是分析大学教职员工和学生（=290人，73.1%为女性）的自尊水平，并设计一种能够根据他们的应对策略、心理韧性和社会人口统计学变量预测这些水平的算法。为此，使用了罗森伯格自尊量表（RSES）、感知压力量表（PSS）和简易心理韧性量表。结果表明，心理韧性和感知压力在预测参与者的自尊水平方面具有重要作用。研究结果凸显了人工神经网络在预测教育中的心理变量方面的有用性。
