GULLIVER: A decision-making system based on user observation for an adaptive training in informed virtual environments

Modern training through virtual environments is widely used in transport in order to provide a high level of precision and more and more complex situations. These virtual environments provide training scenarios with automatic and repetitive feedback to the trainees. Experienced learners receive too many aids and novice learners receive too few. In this research work, inspired by trial and error pedagogy, we have designed and evaluated a fluvial-navigation virtual training system which includes our GULLIVER module to determine the most appropriate level of feedback to display for learner guiding. GULLIVER is based on a decision-making module integrating uncertain data coming from observation of the learner by the system. An evidential network with conditional belief functions is used by the system for making decision. Several sensors and a predictive model are used to collect data in real time. Metaphors of visualization are displayed to the user in an immersive virtual reality platform as well as audio feedback. GULLIVER was evaluated on 60 novice participants. The experiment was based on a navigation case repetition. Two major results are the following: (i) the learners get experience and error awareness from the virtual navigation with our system and (ii) they show their capacity to navigate after the training and the better performance of the GULLIVER system.