Abstract
The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. In many implementations of automatic classifiers finding the right student learning style represents the hardest assignment. The reason is that most of the techniques work using expert groups or a set of questionnaires which define how the learning styles are assigned to students. This paper presents a novel approach for automatic learning styles classification using a Kohonen network. The approach is used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the author tool.
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Zatarain-Cabada, R., Barrón-Estrada, M.L., Zepeda-Sánchez, L., Sandoval, G., Osorio-Velazquez, J.M., Urias-Barrientos, J.E. (2009). A Kohonen Network for Modeling Students’ Learning Styles in Web 2.0 Collaborative Learning Systems. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_45
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DOI: https://doi.org/10.1007/978-3-642-05258-3_45
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