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Self-Esteem at University: Proposal of an Artificial Neural Network Based on Resilience, Stress, and Sociodemographic Variables.
Martínez-Ramón, Juan Pedro; Morales-Rodríguez, Francisco Manuel; Ruiz-Esteban, Cecilia; Méndez, Inmaculada.
Afiliación
  • Martínez-Ramón JP; Department of Evolutionary and Educational Psychology, Faculty of Psychology, Campus Regional Excellence Mare Nostrum, University of Murcia, Murcia, Spain.
  • Morales-Rodríguez FM; Department of Evolutionary and Educational Psychology, Faculty of Psychology, Cartuja Campus, University of Granada, Granada, Spain.
  • Ruiz-Esteban C; Department of Evolutionary and Educational Psychology, Faculty of Psychology, Campus Regional Excellence Mare Nostrum, University of Murcia, Murcia, Spain.
  • Méndez I; Department of Evolutionary and Educational Psychology, Faculty of Psychology, Campus Regional Excellence Mare Nostrum, University of Murcia, Murcia, Spain.
Front Psychol ; 13: 815853, 2022.
Article en En | MEDLINE | ID: mdl-35295381
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 (N = 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Psychol Año: 2022 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Psychol Año: 2022 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza