Helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations

Abstract

Adapting training in real time can be challenging for instructors. Real-time simulation can present rapid sequences of events, making it difficult for an instructor to attribute errors or omissions to specific underling gaps in skills and knowledge. Monitoring multiple students simultaneously imposes additional attentional workload on an instructor. This challenge can be further exacerbated when an instructor’s view of the student is obscured by virtual reality (VR) equipment. To support instructors’ ability to adapt training, Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during training simulations and offer recommendations for restoring lapses in engagement. We have created a system, called the Observational Motivation and Engagement Generalized Appliance (OMEGA), which we tested in the context of a new U.S. Air Force approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN). PTN integrates traditional flying sorties with VR-enabled ground-based training devices to achieve training efficiencies, improve readiness, and increase throughput. The virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We created a testbed for data capture to construct the ML models, based on theoretical foundations we developed previously. Our research explores OMEGA’s potential to help alert an instructor pilot (IP) to student distraction by flagging attention and engagement lapses. Our hypothesis is that OMEGA could help an IP adapt learning, and potentially manage multiple students at the same time, with alerts of lapsed attention and recommendations for restoring engagement. To test this hypothesis, we ran pilots through multiple PTN scenarios to create data for training the model. In this paper, we report on work to create machine learning models using three different techniques, and present model performance data using standard machine learning metrics. We discuss the modeling approach used to generate instructor recommendations. Future work will present results from a formative evaluation using instructor pilots. These early findings provide preliminary validation for 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.