Educational content adaptation in virtual courses using a fuzzy inference system with emphasis on student’s cognitive characteristics
Keywords:
Fuzzy inference systems, adaptive virtual courses, student’s cognitive characteristics, FSLSM & RCMT testsAbstract
One of the main desirable characteristics ofvirtual courses is to allow students for havingacustomized teaching/learning experience basedon adaptive mechanisms. This article specificallyfocus on the adaptation of educational contentand more specifically in the selection of learningobjects with emphasis on the followingstudents’cognitive characteristics: their learning stylesand their dominant brain hemispheres. In orderto carryout such a process we propose a fuzzyinference system based on FSLSM (Felder andSilverman Learning Style Model) and TQDT(Triadic Quotient Detector Test) tests that areused as mechanisms for measuring the students’cognitive characteristics.
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References
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2. Brusilovsky, P. & Peylo, C. (2003). Adaptiveand Intelligent Web-based Educational Systems.International Journal of Artificial Intelligence in Education,Vol. 13, pp. 156-169.
3. Peña, C. (2004) Intelligent agents to improve adaptivityin a Web-based learning environment. PhD dissertation,Universitat de Girona, Girona, España.
4. IEEE (2002). Draft Standard for Learning ObjectMetadata. En http://ltsc.ieee.org/wg12/files,Consultado en julio 9 del 2011.
5. Arias, F., Moreno, J, Ovalle, D. (2009). Modelo deinferencia difusa para la selección de objetos de aprendizaje encursos virtuales. Reunión Nacional y ExpoingenieríaACOFI 2009. Santa Marta, Colombia.
6. Keefe, J. (1979). Learning style: An overview.In: NASSP’s Student learning styles: Diagnosing andproscribing programs, Reston, USA, pp. 1-17, 1979.
7. Stash, N. (2007). Incorporating Cognitive/LearningStyles in a General-Purpose Adaptive HypermediaSystem. PhD dissertation, Technische UniversiteitEindhoven, Eindhoven, 2007.
8. Chen, S. & Macredie, R. (2002). Cognitive Stylesand Hypermedia Navigation: Development of aLearning Model. Journal of the American Society forInformation Science and Technology, 53(1), pp. 3-15.
9. De Gregori, W. (1999). En busca de una nuevanoología. Estudios Pedagógicos, No 25, pp. 71-82.
10. Felder, R. & Silverman, L. (1988). Learning andteaching styles in engineering education. EngineeringEducation, 78(7), pp. 674-681.
11. Zadeh, L. Fuzzy sets. (1965). Information andControl, 8 (3), pp. 338-353.
12. Kosko, B. (1995). Pensamiento Borroso, la cuevaciencia de la lógica borrosa. Crítica, Barcelona, 1995.13. Bojadziev, G. & Bojadziev, M. (1997). Fuzzylogic for business, finance, and management. WorldScientific Publishing Co.
14. Moreno, J., Arias F. & Ovalle D. (2009). CIA:Framework for the creation and management of AdaptiveIntelligent Courses. WCCE - World Conference onComputer in Education, Brasil.
15. Duque, N. (2009) Modelo adaptativo multiagentepara la planificación y ejecución de cursos virtualespersonalizados. Tesis de Doctorado, Medellín:Universidad Nacional de Colombia - Sede Medellín.
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Published
2013-06-01
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Articles
How to Cite
Educational content adaptation in virtual courses using a fuzzy inference system with emphasis on student’s cognitive characteristics. (2013). Avances: Investigación En Ingeniería, 9(1), 59-65. https://revistas.unilibre.edu.co/index.php/avances/article/view/2735
