Among the problems that face ITS designers, the problem of measuring the student knowledge state after concept learning in order to initially adapt a skill acquisition session according to a student's own necessities is a hard one. Typical approaches are the use of some sort of test to assess the student knowledge and choose an initial set of parameters for a session, or use, regardless the particular necessities of a student, a pre-defined set of initial parameters. We consider the fromer to be disrupting for learning and the latter too simple to deal with the broad possibilities that are faced. It is known that students show different behaviors during concept learning depending on the experience, background and actual understanding (the way a student is understanding a concept) during concept learning. Our approach here is to classify the different behaviors through fuzzy proposition and link them with a student model through fuzzy rules to use in an expert system, and with it, select the most suitable problem-solving strategy for each particular student in order to clear his misunderstandings and facilitate the learning of problem-solving skills. The use of probabilistic reasoning (i.e. Bayesian statistics) instead of fuzzy logic is not suitable for the present situation because of the rigidity and precision of the rules that do not allow a proper manipulation of the vagueness involved in the student behavior. We apply this idea to a circuit analysis ITS where the concept learning session is carried out on a Hypertext environment and the skill acquisition session on an interactive problem-solving environment. By tracing the student use of the Hypertext environment we can know the student behavior and use it as a premise in the fuzzy inference.