Automatic Student Personalization in Preferred Learning Categories

We present a computer-based system for the automatic generation and personalization of courses. The system takes trace of the student’s behavior by analyzing the results of suitable on-line tests. The tests are used to infer information on both the student’s knowledge about the topics of the system’s domain and her/his learning preferences. Both kinds of data are stored in the Student Model and subsequently used in order to customize future courses. The pedagogical approach we have adopted refers to the Felder and Silverman’s proposal in which both learning material and students are categorized in different teaching/learning style classes and then matched together