As multinational joint military exercises become more complex—and readiness preparation for multidomain warfighting ever more essential—the need grows exponentially for training events to utilize adaptive learning and enabled learning analytics to effectively measure performance achievement.

The Synthetic Training Environment Experiential Learning for Readiness (STEEL-R) system aims to provide the solution, producing actionable results across complex multi-platform asynchronous learning and performance data feeds. In the Maturing Advanced Distributed Learning in Exercises (MADLx) project, the Jefferson Institute developed and tested a prototype performance data analytics and return-on-investment dashboard for use in exercises.

The STEEL-R system builds upon that methodology to revolutionize the quality and utility of training performance assessment and empower commanders with the intuitive, relevant analytics they need to make informed decisions—before, during, and after an exercise event. The STEEL-R in Exercises (STEEL-Rx) project will push the boundaries of the system as an emerging capability, conducting the field-based validation testing necessary to deploy STEEL-R at scale.

  • Synthetic Training Environment
  • Experiential Learning
  • Readiness
  • In exercises
STEEL-Rx will work with exercise planners, observers/trainers, and trainees at a series of multinational joint computer-assisted exercises (CAX) and live exercises (LIVEX) over sixty months. It will be an iterative process, with each exercise serving as a milestone and every training event building upon the lessons learned from that which preceded it. In addition to increasing learning efficiency, expanding readiness reporting, and improving learning outcomes at exercise events, STEEL-Rx aims to infuse assessment thinking into exercise planning and scenario design, with clear training objectives articulated early and exercises focused on educating toward those learning goals. STEEL-Rx will similarly further the capacity to align performance assessment with long-range training objectives and competencies, and it will promote the adoption of best practices and interoperable learning analytics standards such as the Experience Application Programming Interface (xAPI).