Modeling student's learning styles in web 2.0 learning systems

The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of thesuccess in the teaching process. In many implementations of automatic classifiers finding the right student learning styler e p r e s e n t s t h e h a r d e s t a s s i g n m e n t . T h e r e a s o n i s t h a t m o s t o f t h e t e c h n i q u e s w o r k u s i n g e x p e r t g r o u p s o r a s e t o fquestionnaires which define how the learning styles are assigned to students. This paper presents a novel approach forautomatic learning styles classification using a Kohonen network. The approach is used by an author tool for buildingIntelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together withthe neural network can also be exported to mobile devices. We present different results to the approach working under theauthor tool.