Our research question was whether we could develop a feasible technique, using Bayesian networks, to diagnose gaps in student knowledge. Thirty-four college-age participants completed tasks designed to measure conceptual knowledge, procedural knowledge, and problem-solving skills related to circuit analysis. A Bayesian network was used to model the knowledge dependencies among the circuit analysis concepts. Preliminary results suggested that the Bayesian network was generally working as intended. When high- and low-performing groups were formed on the basis of posterior probabilities, significant group differences were found favoring the high-performing group with respect to circuit definitions and circuit analysis problems, for both actual and self-assessments, and higher major GPA. The Bayesian network was able to predict participants' performance on a problem-solving item on average 75% of the time. The findings of this study are promising for our long-term goal of developing scalable and feasible online automated reasoning techniques to diagnose student knowledge gaps. (Contains 12 tables and 2 figures.) [Appended are: (1) Node-Voltage Analysis Problem-Solving Procedure (Kaiser, 2003); and (2) Bayesian Network.]