ACTIVE: Adaptive Contextualized Training in Interactive Virtual Environments

An extensive body of literature has emerged on adaptive training systems that increase learning gains by adjusting instructional content or feedback to individual learner needs. A second body of literature has demonstrated some of the benefits of training in extended reality (XR) environments, not all of which have been realized. Regrettably, there has yet to be a comprehensive, generalizable, theory-driven approach to delivering adaptive training in XR environments in a way that fully exploits the benefits of these technologies. The aim of this proposal is to synthesize basic science and relevant theory of XR learning with practical considerations for assessing whether students are meeting learning objectives and adapting to best support each student’s progress. We propose to develop an Adaptive Contextualized Training in Virtual Environments (ACTIVE) framework to provide a roadmap from XR learning theory and instructional design to the real-time measurement and monitoring of student competency on XR tasks that inform instructor adaptation of learning content. The ACTIVE framework will be based on the 4E Cognition model (cognition as embodied, enactive, embedded, and extended) that identifies eight affordances of XR training, such as interactivity and doing rather than only observing. The framework will map these XR affordances onto the kinds of learning objectives, training interventions, and measures that XR is uniquely suited to support. This, in turn, will help select the parts of an instructional module that would most benefit from adaptive XR training. Application of the framework would start with a set of learning objectives from which one would select appropriate training interventions, operationalize learner competency, and identify competency thresholds to trigger or notify the instructor of the need for adaptations. This effort will demonstrate the utility of the ACTIVE framework using the case of Basic Electricity and Electronics (BEE); one of our team members is developing adaptive content and an instructor dashboard for BEE as part of the Ready Relevant Learning effort. We will then design an adaptive XR prototype that instantiates the ACTIVE framework as the means to support instructors in monitoring student performance and adapting instruction in XR or other learning contexts. The ACTIVE framework will be used to guide the development of an adaptive XR prototype in Phase II.