RAPID: Realistic Assessment of Performance in Devices
ABSTRACT: With the growing demands for Remotely Piloted Aircraft (RPA) operations, it becomes increasingly important to determine how to best train RPA crewmembers. However, little is known regarding the effectiveness tradeoffs of training environments. Current tools and processes for evaluating effectiveness lack the flexibility and automation required for providing quick, clear, and useful guidance to support evolving missions and training environments. Thus, Realistic Assessment of Performance in Devices (RAPID) is a data-driven, model-based decision support tool that comprehensively analyzes the effectiveness of existing and future training solutions and provides training stakeholders with clear and quick recommendations on how to effectively train RPA Pilots and Sensor Operators. More specifically, the tool provides guidance on the training environments and/or devices best suited to achieve specified training requirements and how current devices can be modified to better address training requirements. The core decision model underlying the RAPID Tool analyzes the effectiveness tradeoffs based on relationships between training requirements and devices/environments. Once integrated with a performance measurement system, the decision model can be automatically and continually refined with multimodal data to support current and future tradeoff analyses, provide blended learning solutions, and quickly assess how well training devices and environments will achieve desired learning outcomes. BENEFIT: The results of our Phase II activities will be the RAPID Tool, an automated and adaptable tool that facilitates rapid, data driven analyses of the effectiveness of training environments and devices in relation to RPA training requirements. Specifically, the RAPID Tool will facilitate and house the automatic processing of multimodal data to continually refine and validate its underlying model of the relationships of relevant training environment characteristics and devices to training requirements. In support of instructors and training developers, the RAPID Tool will provide recommended training strategies for achieving desired outcomes effectively and efficiently, leveraging a variety of training environments, tools, and devices as applicable. To acquisition professionals, the RAPID Tool will provide a data driven analysis of the training effectiveness tradeoffs of current and future devices, including consideration of non-traditional fidelity variables that are important in RPA training environments. While we continue to develop a tool customized to meet the needs and challenges of Air Force RPA operations, our approach is designed to be generalizable and applicable to other Department of Defense agencies and commercial organizations that share some common challenges with the Air Force: (1) high demand for achieving training objectives in environments besides live missions (e.g. high consequences for errors); (2) uncertainty regarding which training devices, environments, and methods effectively train which training objectives; and (3) limited resources to achieve operational training objectives.
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