Abstract
Personalization according to specific requirements of an individual student is one of the most important features inadaptive educational systems. Considering learning and how to improve a student’s performance, these systems mustknow the way in which an individual student learns best. In this context, the current work outlines a new approach toautomatically and dynamically discover students learning styles, considering its non-deterministic and non-stationaryaspects, and taking into account that learning styles may change during the learning process in an unexpected andunpredictable way. Our approach is mainly based in genetic algorithms and reinforcement learning, and it has beentested through computer simulation of students. Promising results have been obtained through experiments. Someof them are presented in this paper.
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