Fuzzy student modeling for personalization of e-learning courses

In the context of e-learning courses, personalization is a more and more studied issue, being its advantage in terms of time and motivations widely proved. Course personalization basically means to understand student's needs: to this aim several Artificial Intelligence methodologies have been used to model students for tailoring e-learning courses and to provide didactic strategies, such as planning, case based reasoning, or fuzzy logic, just to cite some of them. Moreover, in order to disseminate personalised e-learning courses, the use of known and available Learning Management System is mandatory. In this paper we propose a fine-grained student model, embedded into an Adaptive Educational Hypermedia, LS-Plan provided as plug-in for Moodle. In this way we satisfy the two most important requirements: a fine-grained personalization and a large diffusion. In particular, the substantial modification proposed in this contribution regards the methodology to evaluate the knowledge of the single student which currently has a low granularity level. The experiments showed that the new system has improved the evaluation mechanism by adding information that students and teachers can use to keep track of learning progress.