Student modeling—the task of building dynamic models of student ability—is fraught with uncertainty, caused by such factors as multiple sources of student errors, careless errors and lucky guesses, learning and forgetting. Various approaches have been developed in recent years to make student modeling more tractable. One approach, which is based on fuzzy set theory, aims at building imprecise, or "fuzzy" diagnostic student models; (eg. [18]). We have built upon this approach by developing techniques for representing and updating discrete student knowledge variables in our avionics troubleshooting tutor, Sherlock II. We describe these techniques and, more broadly, the student modeling component in this tutor. Future work will focus on calibrating the student modeling knowledge base and updating rules, evaluating the approach, and comparing it with other approaches to imprecise student modeling that we have implemented and are currently developing.