Agile feedback frameworks utilized in competency-based learning (CBL) environments and adaptive instructional systems (AIS) are crucial to support next generation training and learning ecosystems for the military and future warfighters. Current adaptive systems utilizing learner analytics often elicit copious performance data without considering holistic, personal characteristics essential to learning, including prior knowledge, differentiated learning constructs, learner preferences, and scaffolding learning progress through continuous, agile feedback. Without the direct correlation to immediate, actionable feedback and progress measurement within an assessment, learners are ill-equipped for operational readiness within the learning ecosystem.
This paper provides an overview of current CBL evaluation approaches and contemporary issues encompassing universal designs within digital transformations to inform the conceptual development of a Competency-Based Learning Environment Assessment Feedback Framework matric (CB-LEAFFs). Grounded in theories of distributed learning and cognition, CB-LEAFFs intends to provide an adaptive, assessment feedback architecture for capturing interactions between training and learning assessment artifacts by leveraging parallel streams of data and information. Finally, the paper identifies barriers impacting future readiness and concludes with a discussion of future CB-LEAFFs development and research.