In this paper, a neural network-based fuzzy modeling approach to assess student knowledge is presented. Fuzzy logic is used to handle the subjective judgments of human tutors with respect to student observable behavior and their characterizations of student knowledge. Student knowledge is decomposed into pieces and assessed by combining fuzzy evidences, each one contributing to some degree to the final assessment. The neuro-fuzzy synergism helps to represent teacher experience in an interpretable way, and allows capturing teacher subjectivity. The proposed approach was used to assess knowledge and misconceptions of simulated students interacting with the exploratory learning environment “Vectors in Physics and Mathematics”, which is used by high school pupils to learn about vectors. In our experiments, this approach provided significant improvement in student diagnosis compared with previous attempts.