Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine ‘‘a priori’’ probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable nonempirical mathematical determination of conditional probabilities, while ‘‘a priory’’ probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining ‘‘a priori’’ probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.