Optimizing the Mix between Virtual and Live Military Training

This paper describes the development of a decision algorithm for determining what military tasks can be taught virtually (e.g., simulator, advanced distributed learning) and which tasks should only be taught in classroom or field environment (live). The decision algorithm, based on a DoD study, addressed both individual and collective tasks across the military Services. The goal was to develop a user-friendly system to aid military training developers in making 'first-cut' decisions about training delivery methods, specifically live or virtual. To develop the algorithm, we first examined thousands of military training tasks, reviewed the literature on training tasks and developed a rating system to categorize tasks. The categorization scheme resulted in a variety of task classes with each class encompassing common training characteristics (e.g., level of interactivity or availability of feedback). We conducted an extensive review of the research literature and developed rating factors, which formed the basis of the live vs. virtual decision model. We then drew a random sample of 302 military tasks, categorized the tasks and then applied the rating factors to each task category. Next, using the rating factors we developed a decision algorithm for determining whether each class of tasks can be adequately trained using virtual technologies (costs withstanding) or whether it would be necessary to train the task in a live application. The algorithm is based on a variety of elements from established, peer-reviewed research, current technology, and current military practices. Finally, we applied the algorithm to the task categories developed earlier in the project and conducted an initial validation of the algorithm with training developers. In addition to describing the development and validation process, we will solicit feedback and comments from audience members for consideration during further development, validation, and refinement of the algorithm.