Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application

Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi

Abstract: Virtual Reality (VR) for training and education of real-world tasks has been researched extensively and has growing use in industry. The data generated by trainees in VR could be leveraged to improve the ability to evaluate learning beyond that which is possible in traditional training scenarios. In this paper, we present a machine learning approach that is able to classify users into participants with low-learning (LL) and high-learning (HL) gains, based on a knowledge test, using only the linear and angular velocities of the head-mounted display (HMD) and handheld controllers. To collect this data, we conduct a VR training user study. We demonstrate that even with a limited data set, it is possible to train a machine learning classifier to predict a trainee's learning performance for a given task with high degrees of accuracy and confidence. We investigate three different sets of velocity-based input features and two feature representations in a machine learning experiment. Our results indicate that all feature combinations resulted in high degrees of accuracy and confidence for predicting learning gains in our testing data. By employing a novel visualization technique, we were able to determine that participants with HL gains moved with greater velocities and fewer changes in direction than those with LL gains. These results indicate that it may be feasible to create VR training applications that can predict a user's learning gains and dynamically adapt the training to better support the user's learning, based on commonly available tracking data.