Vol. 8 (2024): Publicación continua
E) Procesos de formación y actores de la educación

Use of Bayesian networks for the analysis of the teaching-learning relationship considering learning styles and teaching methods

PDF (Español)
  • Luz Angélica Aguilar Chávez,
  • Manuel Arnoldo Rodríguez Medina,
  • Xóchitl Graciela Aguilar Rivas
Luz Angélica Aguilar Chávez
Tecnológico Nacional de México – Ciudad Juárez, Chihuahua, México.
Bio
Manuel Arnoldo Rodríguez Medina
Tecnológico Nacional de México – Ciudad Juárez, Chihuahua, México
Bio
Xóchitl Graciela Aguilar Rivas
Tecnológico Nacional de México – Ciudad Juárez, Chihuahua, México.
Bio
Portada-v8i0
DOI: https://doi.org/10.33010/recie.v8i0.1941

Published 2024-01-11

Keywords

  • Learning-teaching,
  • Bayesian networks,
  • teaching styles,
  • learning styles,
  • conditional probability,
  • Bayes’ Theorem
    • ...More
    Less
  • Aprendizaje-enseñanza,
  • redes bayesianas,
  • estilos de enseñanza,
  • estilos de aprendizaje,
  • probabilidad condicional,
  • Teorema de Bayes
    • ...More
    Less

How to Cite

Aguilar Chávez, L. A., Rodríguez Medina, M. A., & Aguilar Rivas, X. G. (2024). Use of Bayesian networks for the analysis of the teaching-learning relationship considering learning styles and teaching methods. RECIE. Revista Electrónica Científica De Investigación Educativa, 8, E1941. https://doi.org/10.33010/recie.v8i0.1941

Copyright (c) 2024

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

The analysis of the tie between teaching styles, as well as those of learning as a fundamental part in scientific education, have become a focus of high attention in the world, an object of study for the training of competent people for the present and for the future of society, so demanded of the generation of analytical and systematic citizens within the scientific field. This document analyzes the way in which both learning styles and teaching methods affect the academic behavior of a student, together with a set of additional variables to the context that directly affect the achievement of students by performing an analysis and evaluation of the results of a sampling through the construction of Bayesian Networks. The present work collects information from 1,531 students and 1,531 teachers belonging to the Tecnológico Nacional de México, Ciudad Juárez Campus, regarding characteristics belonging to each character such as sociodemographics, teaching and learning styles were determined by applying the corresponding tests such as Honey and Alonso for learning styles.

PDF (Español)

References

  1. Almond, R. G., Mislevy, R. J., Steinberg, L. S., Yan, D., y Williamson, D. M. (2015). Bayesian networks in educational assessment. Springer.
  2. Botsios, S., Georgiou, D. A., y Safouris, N. F. (2007). Learning style estimation using Bayesian networks. International Conference on Web Information Systems and Technologies, 2, 415-418.
  3. Carmona, C., Castillo, G., y Millán, E. (2008). Designing a dynamic bayesian network for modeling students’ learning styles. 2008 Eighth IEEE International Conference on Advanced Learning Technologies, 346-350. https://doi.org/10.1109/ICALT.2008.116
  4. Cazau, P. (2004). Estilos de aprendizaje: Generalidades. https://cursa.ihmc.us/rid%3D1R440PDZR-13G3T80-2W50/4.%20Pautas-%20evaluar-Estilos-de-Aprendizajes.pdf
  5. Chiang Salgado, M. T., Díaz Larenas, C., y Rivas Aguilera, A. (2013). Un cuestionario de estilos de enseñanza para el docente de educación Superior. Revista Lasallista de Investigación, 10(2), 62-68.
  6. Conati, C., Gertner, A. S., VanLehn, K., y Druzdzel, M. J. (1997). On-line student modeling for coached problem solving using Bayesian networks. En A. Jameson, C. Paris y C. Tasso (eds.), User modeling. International Centre for Mechanical Sciences, vol. 383 (pp. 231-242). Springer. https://doi.org/10.1007/978-3-7091-2670-7_24
  7. Edwards, W. (1998). Hailfinder: tools for and experiences with Bayesian normative modeling. American Psychologist, 53(4), 416.
  8. Feldman, R. S. (2005). Psicología: con aplicaciones en países de habla hispana. McGraw-Hill.
  9. Fernández, A., Morales, M., Rodríguez, C., y Salmerón, A. (2011). A system for relevance analysis of performance indicators in higher education using Bayesian networks. Knowledge and Information Systems, 27(3), 327-344. https://doi.org/10.1007/s10115-010-0297-9
  10. Furman, M. (2018). La educación científica en las aulas de América latina, el estado de la ciencia 2018. Principales indicadores de ciencia y tecnología iberoamericanos/interamericanos. Ricyt/OEI
  11. García, P., Amandi, A., Schiaffino, S., y Campo, M. (2005). Using Bayesian networks to detect students’ learning styles in a web-based education system. En Proceedings of ASAI 2005, Argentine Symposium on Artificial Intelligence (pp. 115-126).
  12. García, P., Amandi, A., Schiaffino, S., y Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. https://doi.org/10.1016/j.compedu.2005.11.017
  13. Laudadío, M. J., y Da Dalt, E. (2014). Estudio de los estilos de enseñanza y estilos de aprendizaje en la universidad. Educación y Educadores, 17(3), 483-498.
  14. Marchesi, A., y Hernández, C. (coords.) (2003). El fracaso escolar. Una perspectiva internacional. Alianza.
  15. Martin, J., y VanLehn, K. (1995). Student assessment using Bayesian nets. International Journal of Human-Computer Studies, 42(6), 575-591. https://doi.org/10.1006/ijhc.1995.1025
  16. Martínez, I., Renés, P., y Martínez, P. (2019). Los estilos de aprendizaje y de enseñanza: análisis y diagnóstico en educación superior de centro internacional de estudios superiores del español, CIESE-Comillas (España). Revista de Estilos de Aprendizaje, 12(24), 28-41. https://doi.org/10.55777/rea.v12i24.1317
  17. Martínez-Geijo, P. (2002). Categorización de comportamientos de enseñanza desde un enfoque centrado en los estilos de enseñanza [Tesis doctoral inédita. UNED, Madrid].
  18. Mayer, R. E. (2011). Applying the science of learning. Pearson Merrill Prentice Hall.
  19. Mayo, M., y Mitrovic, A. (2001). Optimising ITS behaviour with Bayesian networks and decision theory. International Journal of Artificial Intelligence in Education, 12, 124-153.
  20. Millán, E., Loboda, T., y Pérez-de-la-Cruz, J. L. (2010). Bayesian networks for student model engineering. Computers & Education, 55(4), 1663-1683. https://doi.org/10.1016/j.compedu.2010.07.010
  21. Neapolitan, R. (2004). Learning Bayesian networks. Pearson Prentice Hall.
  22. Nieto, L. E. (2002). Métodos estadísticos bayesianos. http://allman.rhon.itam.mx/~lnieto
  23. Riva Amella, J. L. (2009). Cómo estimular el aprendizaje. Océano.
  24. Tapia, F. (2007). Modelo bayesiano para la optimización y personalizacion de aprendizaje en línea: estudio casuístico [Tesis doctoral, Universidad de las Palmas de Gran Canaria, España].
  25. Wei, H. (2014). Bayesian networks for skill diagnosis and model validation. Annual Meeting of Council on Measurement in Education, Philadelphia, PA. https://www.pearson.com/content/dam/one-dot-com/one.com/global/Files/efficacy-and-research/schools/030_NCME_HW.pdf
  26. Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345- 359. https://doi.org/10.1016/j.compedu.2003.09.005