Modelo de Inferencia Difusa para la Selección de Objetos de Aprendizaje Adaptados a los Perfiles de los Estudiantes

In order to improve students' teaching/learning processes computational systems have been developed allowing to adapt virtual courses based on the students' profiles. There are currently various kinds of adaptation such as instruccional adaptation of plans, assessments adaptation, and adaptation of educational contents, among others.The aim of this paper is to propose a fuzzy inference model for Learning Objects selection, taking into account the students' learning styles and the people's behavior provided by the identification of the brain hemispheres. For the determination of the learning styles, we have adopted the FSLSM (Felder and Silverman Learning Style Model) model and for the identification of the students' behavior the MTQD (Mental Triadic Quotient Detector) model was used. Finally, the model validation and some results obtained are presented.