Detecção e Correção Automática de Estilos de Aprendizagem em Sistemas Adaptativos para Educação

One of the most important features of adaptive e-learning systems is the personalisation according to specific requirements of each individual student. In this paper, we introduce a promising approach for automatic detection and correction of student learning styles (LS) based on Markov Chains. Most of the work in this field presents complex and inefficient approaches. Our approach is based on learning styles combination (LSC) and dynamic correction of inconsistencies in the student model (SM), taking into account the non-deterministic aspect of the learning process. Promising results were obtained from tests, and some of them are discussed in this paper.