The dynamism of the new society forces the professional man to be abreast of technical progress. It is essential to introduce new didactic methodologies based on continuous long-life learning. A good solution can be E-learning. Although distance education environments are able to provide trainees and instructors with cooperative learning atmosphere, where students can share their experiences and teachers guide them in their learning, some problems must be still solved. One of the most important problem to solve is the correct definition of the domain of knowledge (i.e. ontology) related to the various courses. Often teachers are not able to easily formalize in correct way the reference ontology. On the other hand if we want realize some intelligent tutoring system that can help students and teachers during the learning process starting point is the ontology. In addition, the choice of best contents and information for students is closely connect to the ontology. In this paper, we propose a method for learning ontologies used to model a domain in the field of intelligent e-learning systems. This method is based on the use of the formalism of Bayesian networks for representing ontologies, as well as on the use of a learning algorithm that obtains the corresponding probabilistic model starting from the results of the evaluation tests associated with the didactic contents under examination. Finally, we will present an experimental evaluation of the method using data coming from real courses.
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