Bayesian based adaptive question generation technique

AbstractIn this paper we aim to estimate the student knowledge model in a probabilistic domain using automatic adaptively generated assessment questions. The student answers are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the student's and model answers. Moreover, a comparative study between using the adaptive and random generated questions for updating the student model is investigated. Results suggest that utilizing adapted generated questions increases the approximation accuracy of the student model by 40% in addition to decreasing of the required assessing questions by 35%