Discovering student models in e-learning systems

In all areas of the e-era, personalization plays an important role. Particularly in e-learning a main issue is student modeling, that is the analysis of student behavior and prediction of his/her future behavior and learning performance. In fact, nowadays, the most prevailing issue in the e-learning environment is that it is not easy to monitor students' learning behaviors. In this paper we have focused our attention on the system ( the Profile Extractor) based on Machine Learning techniques, which allows for the discovery of preferences, needs and interests of users that have access to an e- learning system. The automatic generation and the discovery of the user profile, to agree as simple student model based on the learning performance and the communication preferences, allow creating a personalized education environment. Moreover, we presented an evaluation of the accuracy of the Profile Extractor system using the classical Information Retrieval metrics.