Fuzzy System for Automatic Detection of Learning Styles in Web Training Environments
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Keywords

Learning styles
web training environments
adaptability
fuzzy systems
personalization

How to Cite

Palomino Hawasly, M. A., Strefezza, M., & Contreras, L. (2015). Fuzzy System for Automatic Detection of Learning Styles in Web Training Environments. Ciencia, Docencia Y Tecnología, 27(52). Retrieved from https://ojstesteo.uner.edu.ar/index.php/cdyt/article/view/63

Abstract

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
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The authors retain the copyright and grant the journal the right to be the first publication of the work, as well as licensing it under a Creative Commons Attribution License  that allows others to share the work with an acknowledgment of the authorship of the work and publication initial in this magazine. All content is published under the Creative Commons 4.0 international license: Attribution-Non-Commercial-Share Alike.

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