Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications

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Computational neuroscience (CNS) has enabled significant advances in the understanding of cognitive processes through mathematical models and computational simulations, providing a more precise understanding of brain activity. However, its application in higher education remains limited, which restr...

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Detalles Bibliográficos
Autores: Adauto Medina, Willy, Olivares Zegarra, Soledad, Aybar Bellido, Irma, Arones, Maritza, Caycho Salas, Beatriz
Formato: artículo
Fecha de Publicación:2025
Institución:Universidad Nacional San Luis Gonzaga de Ica
Repositorio:UNICA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.unica.edu.pe:20.500.13028/7413
Enlace del recurso:https://hdl.handle.net/20.500.13028/7413
Nivel de acceso:acceso abierto
Materia:brain-computer-interface
computational neuroscience
higher education
neurofeedback
systematic review
https://purl.org/pe-repo/ocde/ford#1.02.00
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dc.title.none.fl_str_mv Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
title Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
spellingShingle Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
Adauto Medina, Willy
brain-computer-interface
computational neuroscience
higher education
neurofeedback
systematic review
https://purl.org/pe-repo/ocde/ford#1.02.00
title_short Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
title_full Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
title_fullStr Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
title_full_unstemmed Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
title_sort Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
author Adauto Medina, Willy
author_facet Adauto Medina, Willy
Olivares Zegarra, Soledad
Aybar Bellido, Irma
Arones, Maritza
Caycho Salas, Beatriz
author_role author
author2 Olivares Zegarra, Soledad
Aybar Bellido, Irma
Arones, Maritza
Caycho Salas, Beatriz
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Adauto Medina, Willy
Olivares Zegarra, Soledad
Aybar Bellido, Irma
Arones, Maritza
Caycho Salas, Beatriz
dc.subject.none.fl_str_mv brain-computer-interface
computational neuroscience
higher education
neurofeedback
systematic review
topic brain-computer-interface
computational neuroscience
higher education
neurofeedback
systematic review
https://purl.org/pe-repo/ocde/ford#1.02.00
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.00
description Computational neuroscience (CNS) has enabled significant advances in the understanding of cognitive processes through mathematical models and computational simulations, providing a more precise understanding of brain activity. However, its application in higher education remains limited, which restricts its potential to optimize teaching, cognitive and emotional regulation, and personalized learning. This study aims to examine the problems addressed by CNS, the methods used, and their implications for higher education, analyzing scientific articles from the ScienceDirect, PubMed, and Scopus databases through a systematic review study following the PRISMA guidelines. The results show that the application of methods such as EEG, BCI, neurofeedback, fNIRS, tDCS, and computational models has facilitated the adaptation of content and the assessment of cognitive load in students. However, its implementation still faces methodological, economic, and technological barriers, such as variability in neural responses and limited accessibility. It is concluded that CNS has a high potential to transform higher education, but its effective integration requires the adoption of regulatory and standardized frameworks, which promote the creation of specialized areas in CNS within their departments of psychopedagogy or neuroeducation, in order to promote its development, accessibility, and ethical application in educational environments.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2026-03-31T14:57:57Z
dc.date.available.none.fl_str_mv 2026-03-31T14:57:57Z
dc.date.issued.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13028/7413
dc.identifier.doi.none.fl_str_mv 10.3991/ijoe.v21i08.55225
url https://hdl.handle.net/20.500.13028/7413
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv International Journal of Online and Biomedical Engineering
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dc.publisher.none.fl_str_mv International Federation of Engineering Education Societies (IFEES)
publisher.none.fl_str_mv International Federation of Engineering Education Societies (IFEES)
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spelling Adauto Medina, WillyOlivares Zegarra, SoledadAybar Bellido, IrmaArones, MaritzaCaycho Salas, Beatriz2026-03-31T14:57:57Z2026-03-31T14:57:57Z2025https://hdl.handle.net/20.500.13028/741310.3991/ijoe.v21i08.55225Computational neuroscience (CNS) has enabled significant advances in the understanding of cognitive processes through mathematical models and computational simulations, providing a more precise understanding of brain activity. However, its application in higher education remains limited, which restricts its potential to optimize teaching, cognitive and emotional regulation, and personalized learning. This study aims to examine the problems addressed by CNS, the methods used, and their implications for higher education, analyzing scientific articles from the ScienceDirect, PubMed, and Scopus databases through a systematic review study following the PRISMA guidelines. The results show that the application of methods such as EEG, BCI, neurofeedback, fNIRS, tDCS, and computational models has facilitated the adaptation of content and the assessment of cognitive load in students. However, its implementation still faces methodological, economic, and technological barriers, such as variability in neural responses and limited accessibility. 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