Adapting instruction by measuring engagement with machine learning in virtual reality training

Abstract

The USAF has established a new approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN) that integrates traditional flying sorties with VR-enabled ground-based training devices and data-driven proficiency tracking to achieve training efficiencies, improve readiness, and increase throughput. Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during any computer-mediated training (simulation, courseware) and offer recommendations for restoring lapses in engagement. We are currently developing and testing this approach, called the Observational Motivation and Engagement Generalized Appliance (OMEGA) in a PTN context. Two factors motivate this work. First, one goal of PTN is for an instructor pilot (IP) to simultaneously monitor multiple simulator rides. Being alerted to distraction, attention and engagement can help an IP manage multiple students at the same time, with recommendations for restoring engagement providing further instructional support. Second, the virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We have created a testbed for data capture in order to construct the ML models, based on theoretical foundations we developed previously. We are running pilots through multiple PTN scenarios and collecting formative data from instructors to evaluate the utility of the recommendations OMEGA generates regarding how lapsed engagement can be restored. We anticipate findings that validate the use of ML models for learning to detect engagement from the rich data sources characteristic of virtual environments. These findings will be applicable across a broad range of conventional and VR training applications.