@misc{10481/78013,
year = {2022},
month = {2},
url = {https://hdl.handle.net/10481/78013},
abstract = {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.},
publisher = {Frontiers},
keywords = {Artificial neural network},
keywords = {Educational psychology},
keywords = {Professor},
keywords = {Resilience},
keywords = {Self-esteem},
keywords = {Stress},
keywords = {University student},
keywords = {Artificial intelligence},
keywords = {Inteligencia artificial},
title = {Self-Esteem at University: Proposal of an Artificial Neural Network Based on Resilience, Stress, and Sociodemographic Variables},
doi = {10.3389/fpsyg.2022.815853},
author = {Martínez Ramón, Juan Pedro and Morales Rodríguez, Francisco Manuel},
}