Repurposing a Digital Twin-Based Decision Support System as a Training Tool by Incorporating Learning Science Principles

Abstract

The U.S. Navy has a sophisticated manpower planning process for recruiting, selecting, training, and placing thousands of enlisted Sailors into hundreds of specific jobs every year. Given the large number of “moving parts” in this manpower planning process, some inefficiencies are unavoidable. For example, there may be a non-trivial time gap between when some Sailors complete their job-specific training course and when they arrive at their duty station. During this time, their knowledge and skills will invariably decay due to lack of use. Electronic job aids, such as Interactive Electronic Technical Manuals (IETMs) and Decision Support Systems (DSS), are important to mission success because they help to reduce the need for Sailors to memorize rarely used factual details. However, IETMs and DSSs are job aids, not training tools. In this paper, we describe how we repurposed an existing digital twin-based DSS to create a ubiquitous, online training tool. We did so by leveraging the learning science principles of human performance measurement, adaptive learning with scaffolding, performance feedback, and training data analytics. We conclude with a series of best practices and lessons learned that other practitioners could follow when attempting to repurpose electronic job aids that were not originally designed for training.