Abstract
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.
Highlights
Today, new technologies of information and communication invaded us in our workplaces, our schools and universities and more than ever necessary in all areas, namely, the field of education
Brusilovsky et al in [1] and [2] provides techniques and methods of adaptation based on the model of the user, Piombo [3] proposes a solution based on Bayesian networks for adaptation of learning activities, Zinber [4] proposes a service-oriented solution based on ontologies for the generation of personalized courses, Masun Nabhan [5] used Fuzzy -ART2, neural networks and fuzzy logic to provide to the learners an adaptive learning environment, Markowska-Kaczmar et al [6] used artificial intelligence techniques to better identify the characteristics of learners in order to improve the quality of personalized learning resources
In this paper we have presented a solution based on Bayesian networks and the Semantic Web for the construction of evolutionary personalized learning paths
Summary
New technologies of information and communication invaded us in our workplaces, our schools and universities and more than ever necessary in all areas, namely, the field of education These technologies have opened new access routes to knowledge. Bayesian networks are used in many areas and have been successful in applications where there is decision making In this solution our probabilistic model aims to make decisions about the choice of hypermedia units for the construction of evolutionary personalized learning paths. This solution has the pretension to help learners, lamenting the difficulties or gaps, develop new concepts or improve their knowledge or expertise. Learners can use the tool outside the classroom by targeting directly the objectives to understand while taking advantage of an adaptation that takes into account their knowledge and learning styles during the generation of learning paths
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