Gaussian Naive Bayes for Online Training Assessment in Virtual Reality-Based Simulators

Training systems based on virtual reality are used in several areas, as in the medical sciences. In these systems the user is immersed into a virtual world to have realistic training through realistic interactions. In such training is important to know the quality of user's training and by didactic reasons the user must receive his/her assessment immediately after of end of training. For this reason, an online assessment system allows the user to improve his/her learning because it can identify, where he committed mistakes or presented low efficiency. Several approaches to perform assessment in training simulators based on virtual reality have been proposed. In this paper, we present a new approach to online training assessment based on Gaussian Naive Bayes for modeling and classification of simulation in M pre-defined classes. Gaussian Naive Bayes is a generalization of Naive Bayes Networks, which are a special case of probabilistic networks that allows treating continuous variables.