Sistema Difuso Para la Detección Automática de Estilos de Aprendizaje en Ambientes de Formación Web

This paper shows a fuzzy system to detect learning styles through a virtual training environment, with the aim of contributing to improved levels of personalization. Here, the individual learning characteristics become the main ingredient scenarios innovative virtual training. It is shown three factors that were taken into account when formalizing the fuzzy variables: an adaptation of Felder and Silverman test, the path or trace of learning and a knowledge test. They explain the nature and respective connotation of each of them as the criteria for the construction of fuzzy production rules. Also, they show some of the results obtained when simulating with various input data