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First of all, and to clarify our purpose, it seems important to say that the work we are presenting here lie within the framework of learner modeling in an adaptive system understood as computational modeling of the learner .we must state also that Bayesian Networks are effective tools for learner modeling under uncertainty. They have been successfully used in many systems, with different objectives, from the assessment of knowledge of the learner to the recognition of the plan followed in problem solving. The main objective of this paper is to develop a Bayesian networks for modeling the learner from the use case diagram of the Unified Modeling Language. To achieve this objective it is necessary first to ask the Why and how we can represent a Learner model using Bayesian networks? How can we go from a dynamic representation of the learner model using UML to a probabilistic representation with Bayesian networks? Is this approach considered experimentally justified? First, we will return to the definitions of the main relationships in the diagram use cases and Bayesian networks, and then we will focus on the development rules on which we have based our work. We then demonstrate how to develop a Bayesian network based on these rules. Finally we will present the formal structure for this consideration. The prototypes and diagrams presented in this work are arguments in favor of our objective. And the network obtained also promotes reusing the learner modeling through similar systems.

Developement of Bayesian Networks from Unified Modeling Language for Learner Modelling