Architecture for the adaptation of learning paths based on ontologies and Bayesian networks ISSN 2319 - 5975

Several researches in the field of education have shown that taking into account learning styles has drastically improved the quality of teaching / learning. The adaptation of the course into the profiles and preferences of learners requires the collection of more information on learners, learning styles and educational resources. To identify the learning style of each learner, the architecture is designed to require the learner to pass the test of Felder and Silverman in his first connection. This test provides information about the preferences of learning styles of the learner. Our contribution in this paper consists of an adaptive approach based on the semantic web and Bayesian networks (BN), to provide learners with personalized courses according to their profiles and learning objectives. In addition, the system allows to make a diagnosis and classification of errors made by learners to generate relevant remedial course. Indeed, this model allows learners, teachers and instructional designers to work with software agents to automatically and effectively build custom routes oriented educational goals.