The US Army STE Experiential Learning - Readiness (STEEL-R) project aims to improve training effectiveness in training environments by tracking progress on skill and competency acquisition through multiple developmental stages. Its data strategy involves translating experience API (xAPI) statements that report performance into assertions about competencies and skills in a competency framework, and then using these assertions to estimate the competency state of each soldier and unit. This paper details three critical and broadly applicable processes used to implement this strategy that have not been previously detailed in publications:
- The process used to ensure that STEEL-R made the crucial and surprisingly subtle distinction between measuring performance on a task and estimating to what extent learners possess the competencies and skills required for a task. This paper explains and illustrates this distinction and how the STEEL-R project helped analysts understand and apply it.
- The process used to construct competency frameworks from doctrine. STEEL-R uses multi-level frameworks that incorporate principles from learning science and include transferable skills such as leadership and teamwork. This paper details how these frameworks were developed and provides a recipe that others can use for similar purposes.
- The process used to translate activity data and performance measures into assertions about competencies. STEEL-R maps performance measures to skills and competencies with “strengths' that reflect the relevance and trustworthiness of the measures. This paper explains how this mapping was defined and implemented.
The processes detailed in this paper have been used by the authors in Army, Navy, Air Force and civilian applications. They are fundamental to any effort to express people’s capabilities in digital records that identify their granular competencies and skills. The final section of the paper puts this into the context of the digital transformation that is affecting all aspects of military and civilian life and discusses how these processes are expected to be refined and modified as this transformation progresses.
Keywords
ASSESSMENT, BEHAVIOR MODELING, COMPETENCY BASED TRAINING, ENHANCING PERFORMANCE, PEDAGOGICAL DESIGN, TRAINING, XAPI
Additional Keywords
Performance Analytics, Competency Frameworks, Competency Modeling, Competency Estimation, STEEL-R