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A Probabilistic Approach for the Generation of Learning Sessions Tailored to the Learning Styles of Learners

In this paper we present an audacious solution based on Bayesian networks and educational approach for the construction of evolutionary personalized learning paths. We mean by evolutionary learning paths, paths that are composed gradually as learners advance in their learning, i.e in real time. To do this, the system selects the hypermedia units of learning to apprehend based on the results of formative assessments, psychological and cognitive characteristics of learner. 
The architecture that we propose is based, firstly, on the semantic web, First, in order to model the domain model and to index learning resources so as to maximize their reuse, and then to represent the personal and cognitive traits of learners in a learner model while integrating their learning styles according to the Felder and Silverman model; and secondly, a probabilistic approach based on Bayesian networks that calculates the probability of success of each candidate hypermedia unit, for selecting those who are most appropriate for the construction of evolutionary personalized learning paths. 
The proposed Bayesian model is validated with real data collected from an experimental study with a specimen of students.