Pattern Recognition for the Identification of Learning Styles on Educational Mobile and Social Network Tools

In this paper we present an implementation of pattern recognition techniques as the central part of an adaptive learning social network to be used as an authoring and learning tool. With this tool, adaptive courses, intelligent tutoring systems and lessons can be created, displayed and shared in collaborative and mobile environments by communities of instructors and learners. To show the maturation process to end up with the adaptive tool called Zamna, we first show the development of three previous intelligent tutoring systems with authoring and personalizing capabilities. In most of them the Felder-Silverman model is followed to tailor courses to the student's learning style. Several pattern recognition approaches are applied to identify the student's learning style. An introduction of a social learning network to create, view and manage adaptive intelligent tutoring systems, and some innovative methods to identify the student's learning style, are the contributions of this paper.

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