• Conference Proceeding

Determining the Right Mix of Live Virtual, and Constructive Training

Citation

Frank, G., Helms, R. F., & Voor, D. (2000). Determining the Right Mix of Live Virtual, and Constructive Training. In Proceedings of the Interservice/Industry Training Systems and Education Conference,.

Abstract

The use of a mixture of live, virtual, and constructive training has become accepted practice for training within the Department of Defense. We call training environments that use a combination of these techniques an Advanced Learning Environment (ALE). A key issue is getting the right mix of live, virtual, and constructive training in order to achieve cost-effective training. We present a technology-based methodology for task analysis that assists in making the tradeoffs necessary for designing a cost-effective ALE. This technology-based methodology represents an update of traditional Instructional System Design methods that have been used for training analyses. The method divides the training of each task into four steps: Familiarization, Acquiring the skills, Practicing the skills, and Validating the skills. We use the acronym FAPV to refer to these four steps. We have implemented the FAPV analysis with a tool that starts with a database of tasks and training times. The tool allows dynamic tradeoffs across a variety of variables, including student loads, choice of training devices, available facilities, student/instructor ratios, and training device reliability. This paper describes the FAPV analysis and process, and illustrates the results with three examples developed for the US Army. The effectiveness and cost associated with training in live, virtual, and constructive environments can vary significantly. FAPV analysis helps the training developer estimate the impact on training effectiveness and associated costs of the choice of live, virtual, and constructive training. The dynamic variables allows the training developer to make rapid tradeoffs between multiple training environment configurations to select training devices and determine the number of training devices that are required to meet student throughput goals.