Recent engineering requirements for simulation-based training devices and content are focused mainly on a device’s material design, and the software-based rendering engines, terrains and entities needed for these devices to function.  What has been left out of simulation-based training device requirements are the conditions, attributes, data and standards of human learning a device must produce, suggesting learning should be the device’s primary quality indicator.  Missing requirements include an ability for trainers or learners to design measurable synthetic experiences for specific learning outcomes, based on the latest learning-science, and the levels of learning evaluation a device must achieve.  This includes measuring against data from comparable live-training experiences.  Another requirement missing in modern simulation-based devices is the ability to extract the human learner’s cognitive and psychomotor data required to independently verify learning requirements are objectively met.  To create and properly manage these types of requirements, the process of learning engineering must be integrated into the modeling and simulation engineering program.

Learning engineering (LE) is the practice of finding science-based, user-centered, and data-informed solutions for human learning needs, problems or challenges.  Very similar to traditional systems engineering, learning engineering provides a system-of-systems approach, and the superposition perspective required to design across the multiple phases of simulation-based training but with the primary objective of improving human learning outcomes.  This includes managing the multiple additional occupations and disciplines required to develop and evaluate simulation devices, and the types of content design and data outputs required to ensure the device’s learning production, and the learner’s return-on-investment is maximized.

This paper will discuss a use-case of the learning engineering process integrated into the design, development and evaluation requirements of simulation-based training device hardware, software, experience design, and learning assessment methods required to achieve a standard of human learning, based on learning-science and inspectable and reproducible data outcomes. 

Keywords
DESIGN, EXERCISE, EXPERIENCE API, LEARNING STANDARDS, MEASURES, SIMULATIONS, SYNTHETIC ENVIRONMENT

Additional Keywords
Experience Design, Learning Engineering, Learning Activity Metadata