Automatic update of e-learning environments based on heterogeneous traces

In e-learning environments, collect data on users from their activities traces is crucial. It permits to improve the adaptation process and the development of pedagogical tools. Modeling users in e-learning environment is largely looked in research. Nevertheless, sharing and reusing user data jointly in different environments is yet overlooked. In this context, the question that arises is: How to update systematically different environments after activities performed by a user in a given environment? In this paper, we address this question and we propose a solution which updates systematically diverse models of different environments based on the activities performed by a user in a given environment.