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A Sustainable Educational Tool for Engineering Education Based on Learning Styles, AI, and Neural Networks Aligning with the UN 2030 Agenda for Sustainable Development

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dc.contributor.authorIsaza Domínguez, Lauren Genith
dc.contributor.authorVelasquez Clavijo, Fabian
dc.contributor.authorRobles Gómez, Antonio
dc.contributor.authorPastor Vargas, Rafael
dc.date.accessioned2024-12-31T11:52:43Z
dc.date.available2024-12-31T11:52:43Z
dc.date.issued2024-10-15
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Sustainability. 2024; 16(20):8923, está disponible en línea en el sitio web del editor: https://doi.org/10.3390/su16208923. The copyrighted version of this article, first published in Sustainability. 2024; 16(20):8923, is available online at the publisher's website: https://doi.org/10.3390/su16208923.
dc.description.abstractThis study addresses the United Nations 2030 Agenda Sustainable Development Goals 4, 8, 10, and 12 by developing a resource-efficient tool that promotes equitable quality education and lifelong learning opportunities, supports decent work and economic growth, reduces inequalities, and ensures sustainable consumption and production patterns. This study contributes to sustainable education by providing a tool that is designed to be easy to use, easy to modify, and resource-efficient, making it accessible to institutions with limited technological resources. The tool uses artificial intelligence and a long short-term memory (LSTM) neural network to provide personalized teaching, adapting to the unique learning styles of its users. A custom survey adapted from the Felder–Silverman model was used to track weekly learning style transitions among 72 engineering students at the Faculty of Engineering at the University of Los Llanos. These data were used to build the LSTM model to predict learning style transitions over a 16-week semester. Two interfaces were created: one for instructors, integrating the LSTM model, and one for students, incorporating a custom survey. An OpenAI API-powered chat was also built into both interfaces to provide study advice to students according to their styles and enable professors to personalize their teaching methodologies in engineering education.en
dc.description.versionversión publicada
dc.identifier.citationIsaza Domínguez LG, Velasquez Clavijo F, Robles-Gómez A, Pastor-Vargas R. A Sustainable Educational Tool for Engineering Education Based on Learning Styles, AI, and Neural Networks Aligning with the UN 2030 Agenda for Sustainable Development. Sustainability. 2024; 16(20):8923. https://doi.org/10.3390/su16208923
dc.identifier.doihttps://doi.org/10.3390/su16208923
dc.identifier.issn2071-1050
dc.identifier.urihttps://hdl.handle.net/20.500.14468/25080
dc.journal.issue20
dc.journal.titleSustainability
dc.journal.volume16
dc.language.isoen
dc.publisherMDPI
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentSistemas de Comunicación y Control
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsengineering educationen
dc.subject.keywordslong short-term memory network (LSTM)en
dc.subject.keywordspersonalized educationen
dc.subject.keywordsartificial intelligence (AI)en
dc.subject.keywordsSustainable Development Goals (SDGs)en
dc.titleA Sustainable Educational Tool for Engineering Education Based on Learning Styles, AI, and Neural Networks Aligning with the UN 2030 Agenda for Sustainable Developmenten
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication17556659-f434-4220-841d-aac35f492e62
relation.isAuthorOfPublicationf93103de-336d-47ac-886b-e2cbd425ed87
relation.isAuthorOfPublication.latestForDiscovery17556659-f434-4220-841d-aac35f492e62
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