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Personalized access to e-learning contents is based on suggesting routes to users considering their preferences. Recent studies show that navigation styles can content much of the information related to cognitive learning styles and preferences. These navigation styles can be defined by means of different parameters. In order to develop a system that provides personalized learning contents, it is necessary to automatically extract student's characteristics and preferences. For that, we use data mining techniques that are methods that, basically, use indicators about student's activity to generate models that can direct the content selection process. In this paper, we address the problem of generating student's learning characteristics and preferences, which can make the task of designing adaptation models easier. This work classifies students in each of the next dimensions: (Active/Reflexive, Deductive/Inductive, Visual/Verbal, Sensitive/Intuitive and Local/Global).
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