Predicting inclusive education training through neuroeducation and universal design for learning
Dados Bibliográficos
| ID | 22162188 |
|---|---|
| Autores | Roque Juan Espinoza Casco (0000-0002-1637-9815, Universidad César Vallejo), Roque Juan Espinoza-Casco (Escuela de Educación, Posgrado, Universidad César Vallejo), César Augusto Mescua-Figueroa (Universidad César Vallejo), Teresa Narváez-Araníbar (Universidad César Vallejo), Consuelo Del Pilar Clemente-Castillo (Universidad César Vallejo), Rafael Romero-Carazas (0000-0001-8909-7782, Catholic University of Santa María) |
| Ano | 2026 |
| Volume | 11 |
| Data de publicação | 2026-07-08 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Frontiers in Education (JOURNAL) |
| Identificadores do periódico | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Editora | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2026.1859627 |
| OpenAlex | W7167671254 |
| Idioma | EN |
| Referências citadas | 59 |
Introduction Contemporary higher education faces the challenge of ensuring inclusive training processes in contexts characterized by the cognitive, cultural, and functional diversity of students. In this scenario, neuroeducation and Universal Design for Learning emerge as complementary approaches that guide equitable pedagogical practices. Accordingly, the aim of this study is to determine the extent to which neuroeducation and Universal Design for Learning contribute to training in inclusive education through machine learning models in the Universidad César Vallejo, Lima, Perú. Methodology A quantitative, applied approach was adopted, with a non-experimental, cross-sectional design. The sample consisted of 129 participants, including faculty members and students. Data were collected using a structured questionnaire comprising 45 Likert-scale items, which demonstrated high reliability ( α = 0.985). The analysis was conducted using statistical techniques and machine learning models, including Random Forest, Gradient Boosting, and neural networks, evaluated through metrics such as AUC, accuracy, and F1-score. Results The findings showed that advanced models achieved high performance levels, with Gradient Boosting and neural networks standing out, reaching values above 0.90. Additionally, variables such as diversity of participation, attention to diversity, and inclusive pedagogical strategies demonstrated greater predictive capacity. Conclusion Neuroeducation and Universal Design for Learning are significantly associated with training in inclusive education and demonstrate substantial predictive capacity within the analyzed sample. However, these findings should be interpreted as predictive evidence rather than proof of causal relationships, given the non-experimental and cross-sectional design of the study. Additionally, the voluntary participation sampling procedure and the predominance of university faculty members in the sample suggest that the results should be generalized with caution beyond similar higher education contexts
Sample size determination · Universal design · Universal Design for Learning · Educational Innovations and Technology · Knowledge Management in Higher Education · Neuroscience, Education and Cognitive Function
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| Velocidade de citação | historical |
|---|---|
| Altamente citado | Não |