Main Subcommittee Category
EducationHow would you label your submission?
AcademiaAbstract
Consistent with the Chief of Naval Operations’ priority to enable sailors to “operate in uncertain, complex, and rapidly changing environments” some of the most advanced educational resources may soon be delivered via immersive extended reality [XR] tools. Immersive XR can simulate the affordances—opportunities for decision and action provided by the environment—of complex situations presenting realistic multi-sensory cues within scenarios that support open-ended action. While XR technology provides capabilities that may improve learning, we argue that it requires thoughtful integration of instructional strategies and learning content embedded within the XR environment to maximize learning outcomes. Unfortunately, no comprehensive, generalizable, theory-driven approach to delivering these interventions yet exists. Here we propose such a framework for delivering effective instruction and assessing real-time learner performance in adaptive XR instruction.
Our framework is based on the notion that cognition, the mental processes underlying learning, emerges through interactions between brain, body, and environment. Using the 4E cognition model, which views cognition as embodied, enactive, embedded, and extended, our framework characterizes the ways humans interact with their environments, laying the foundation for developing effective, authentic learning experiences in XR. We propose that blended adaptive training—the integration of classroom instruction with adaptive technology-aided instruction—is the most practical and cost-effective approach to accelerate learning with XR. We offer an adaptive training intervention framework that aligns affordances, 4E cognition, and instructional interventions to guide development and delivery of XR training. Framework implementation requires metrics that: capture student task competency in an XR environment; reflect intervention thresholds; and, diagnose the cause of performance shortfalls, or successes, so appropriate adaptative remediation or enhancements are selected.
Our proposed framework offers a theory-driven approach to integrating XR scalability, user-centered design, and real-time analytics for adaptive training. Contextualized through a theoretical Navy curriculum case study, we illustrate its potential to enhance learning outcomes and training effectiveness.