Uso de inteligencia artificial para detectar estilos de aprendizaje en estudiantes de educación básica
DOI:
https://doi.org/10.71112/eaktff84Palabras clave:
Inteligencia artificial, estilos de aprendizaje, analítica del aprendizaje, educación básica, personalización educativaResumen
El presente estudio analiza el uso de inteligencia artificial (IA) para identificar estilos de aprendizaje en estudiantes de una institución fiscal de Educación Básica del Ecuador. Se aplicó un enfoque cuantitativo, no experimental y de carácter descriptivo–correlacional, combinando un cuestionario tradicional de estilos de aprendizaje con datos de comportamiento obtenidos mediante una plataforma digital. A través de un modelo de aprendizaje supervisado, la IA clasificó los estilos predominantes a partir de patrones como tiempo de respuesta, secuencia de navegación y preferencia por recursos visuales o verbales. Los resultados evidenciaron una mayor presencia de estudiantes con estilos visuales y activos, mientras que los estilos globales y secuenciales fueron menos frecuentes. El modelo alcanzó métricas sólidas de precisión, exactitud y medida F1, demostrando su fiabilidad para analizar datos en contextos educativos reales. Estos hallazgos confirman que la IA puede convertirse en un recurso estratégico para personalizar la enseñanza, optimizar la planificación docente y atender la diversidad presente en las aulas fiscales. Asimismo, se resalta la importancia de integrar estas tecnologías de manera ética, responsable y pedagógicamente pertinente.
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Bond, M., Zawacki-Richter, O., & Nichols, M. (2021). Systematic review of the application of artificial intelligence in education. Computers and Education: Artificial Intelligence, 2, 100016. https://doi.org/10.1016/j.caeai.2021.100016
Cabero-Almenara, J., & Llorente-Cejudo, C. (2022). Retos del uso de la inteligencia artificial en educación: Una visión desde la competencia digital docente. RIED. Revista Iberoamericana de Educación a Distancia, 25(1), 77–96. https://doi.org/10.5944/ried.25.1.31870
Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedi, P., Lehmann, J., & Fischer, A. (2021). Introduction to neural network-based question answering over knowledge graphs. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(3), e1389. https://doi.org/10.1002/widm.1389
Chen, X., Zou, D., Cheng, G., & Xie, H. (2020). Detecting learning styles using a neural network approach: A learning analytics perspective. Computers & Education, 157, 103968. https://doi.org/10.1016/j.compedu.2020.103968
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2022). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100118. https://doi.org/10.1016/j.caeai.2022.100118
Faizi, R. (2018). Teachers’ perceptions towards using Web 2.0 in language learning and teaching. Education and Information Technologies, 23(3), 1219–1230. https://doi.org/10.1007/s10639-017-9661-7
González-Betancor, S. M., López-Puig, A. J., & Cardenal, M. E. (2021). Digital inequality at home: The school as compensatory agent. Computers & Education, 168, 104195. https://doi.org/10.1016/j.compedu.2021.104195
Hersh, M. (2014). Evaluation framework for ICT-based learning technologies for disabled people. Computers & Education, 78, 30–47. https://doi.org/10.1016/j.compedu.2014.05.001
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Porayska-Pomsta, K., & Holstein, K. (2021). Ethics of AI in education: Towards responsible use. AI and Ethics, 1(1), 1–13. https://doi.org/10.1007/s43681-020-00013-3
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2023). Artificial intelligence in education: Opportunities, challenges, and future directions. Computers and Education: Artificial Intelligence, 4, 100123. https://doi.org/10.1016/j.caeai.2023.100123
Khan, S., Hussain, M., & Yang, S. (2022). Machine learning-based prediction models in education: A systematic review. Computers and Education: Artificial Intelligence, 3, 100080. https://doi.org/10.1016/j.caeai.2022.100080
Khosravi, H., Kitto, K., & Liu, D. Y. T. (2019). Data-driven learning analytics to predict student performance: A systematic review. Journal of Learning Analytics, 6(3), 1–23. https://doi.org/10.18608/jla.2019.63.1
Maier, U., & Klotz, C. (2022). Personalized feedback in digital learning environments: Classification framework and literature review. Computers and Education: Artificial Intelligence, 3, 100080. https://doi.org/10.1016/j.caeai.2022.100080
Mehenaoui, Z., Lafifi, Y., & Zemmouri, L. (2022). Learning behavior analysis to identify learner’s learning style based on machine learning techniques. Journal of Universal Computer Science, 28(11), 1193–1220. https://doi.org/10.3897/jucs.81518
Rasheed, F., & Wahid, A. (2021). Learning style detection in e-learning systems using machine learning techniques. Expert Systems with Applications, 174, 114774. https://doi.org/10.1016/j.eswa.2021.114774
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
Siemon, D., Becker, J., & Eckardt, L. (2020). Early prediction of student performance in higher education using machine learning algorithms. Information Systems and e-Business Management, 18(3), 617–647. https://doi.org/10.1007/s10257-020-00461-3
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2020). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. Educational Technology Research and Development, 67(5), 1235–1273. https://doi.org/10.1007/s11423-019-09671-3
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Derechos de autor 2026 Richard Oswaldo Bravo Loaiza, Karla Michelle Preciado Portocarrera, Yadira Rocío Ordoñez Lapo, Alexandra Patricia Tigrero Martínez, Felipa Eugenia Tello Vera (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
