Abstract:

E-Learning system acquired in the last decade an increasing number of students. In fact, it represents a flexible educational support for the student; it tunes automatically the studied resources according to the individual personality, needs and knowledge of the connected student; it offers a personalized service by analyzing collected data about the student. However, those students often cat off due to some mismatch with their concerns. In fact, e-Learning systems generally rely on the student’s data observed on the e-Learning platform and neglect other sources of information. But, the more information we get, the more we are close to the student and his real expectations. Therefore, we propose, in this paper, the enrichment of the student model by observing his behavior on parallel opened URL(s) to deduce his acquired knowledge and to detect bridge events that require the system intervention for appropriate recommendation to stabilize the student behavior. The main treated issues focus on, firstly, what is the useful information to observe in order to detect the real knowledge acquired by the student? Secondly, how this information is used to enrich the student‘s model?