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Considering learning and how to improve students' performances, an adaptive educa- tional system must know how an individual learns best. In this context, this work presents an in- novative approach for student modeling through probabilistic learning styles combination. Experi- ments have shown that our approach is able to automatically detect and precisely adjust students' learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Be- cause of the probabilistic and dynamic aspects enclosed in automatic detection of learning styles, our approach gradually and constantly adjusts the student model, taking into account students' performances, obtaining a fine-tuned student model. Promising results were obtained from experi- ments, and some of them are discussed in this paper.

A Stochastic Approach for Automatic and Dynamic Modeling of Students' Learning Styles in Adaptive Educational Systems