Adaptation improvement using fuzzy logic

Authors

  • Constanza Raquel Huapaya Departamento de Matemática, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina
  • Leonel Guccione Departamento de Matemática, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina
  • Delia Esther Benchoff Departamento de Matemática, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina
  • Francisco Ángel José Lizarralde Departamento de Matemática, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina
  • Marcela P. Gonzalez Facultad de Psicología, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina

Keywords:

adaptation, personalization, learning styles, fuzzy logic, engineering student

Abstract

A module of a Student Model in a Virtual Learning Environment will be presented in order to promote the personalization of instructional material based on the dynamic knowledge levels and learning styles. The improvement is provided by the inclusion experts' experience in the teaching field whose opinions have been expressed in fuzzy rules using two input linguistic variables (knowledge level and learning style) and two linguistic output variables (difficulty level and individual/group work). These last two variables categorize instructional materials.

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References

[1] Q. Gu , T. Sumner, “Support Personalization in Distributed E-Learning Systems through Learner Modeling”. In: 2nd Information and Communication Technologies, Vol. 1., 2006, pp. 610–615.
[2] F. Tian, Q. Zheng, Q., Z. Gong, J. Du, J., R. Li., “Personalized learning strategies in an intelligent e-learning environment”. In Proceedings of the 11th International Conference on Computing Supported Cooperative Work in Design, 2007, pp. 973-978.
[3] R. Felder, L.K. Silverman, “Learning and teaching styles in engineering education”. Engineering Education, Vol. 78(7), 674-681, 1988.
[4] K. Chrysafiadi , M. Virvou. Advances in Personalized Web-Based Education. Springer Cham Heidelberg. 2014.
[5] P. Brusilovsky, “Adaptive hypermedia”. User Modeling and User Adapted Interaction, Vol. 11(1/2), 2001, pp87-110.
[6] R. Felder, “ Are Learning Styles Invalid? (Hint: No!)”. On-Course Newsletter, September 27, recuperado de: http://www.oncourseworkshop.com/Learning046.htm. 2010.
[7] B. Soloman, R. Felder R. “Index of learning styles questionnaire”. Recuperado el 10 de junio de 2014 de: http://www.engr.ncsu.edu/learningstyles/ilsweb.html
[8] C. Huapaya, M. Gonzalez, E. Benchoff, L.Guccione., F. Lizarralde.”Estimación del Diagnóstico Cognitivo del Estudiante de Ingeniería y su mejora con pruebas adaptativas”. X Congreso de Tecnología en Educación y Educación en Tecnología. 2015. Pp. 480-489.
[9] L. Zadeh. Computing with Words. Principal Concepts and Ideas. Springer. 2012.
[10] G. Gokmen, T. Akincib, M. Tektas.,N. Onat, G. Kocyigita G. “Evaluation of student performance in laboratory applications using fuzzy logic”. Procedia Social and Behavioral Sciences,Vol 2(2),2010, pp. 902-909.

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Published

2015-11-01

How to Cite

Huapaya, C. R., Guccione, L., Benchoff, D. E., Lizarralde, F. Ángel J., & Gonzalez, M. P. (2015). Adaptation improvement using fuzzy logic. Journal of Computer Science and Technology, 15(02), p. 143–148. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/541

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Original Articles