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Improving Task-Operator Analysis for Training Through the Integration of Human Learning Taxonomies and Systems Engineering Models


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Training is a critical part of force sustainment, but the life-cycle cost of recurring training can be quite high. Further, the promotion of the Multi-Capable Airman (MCA) concept leads to questions on how best to train airmen on tasks outside of their core career field. The MCA concept, coupled with continued increase of technology effectiveness, incentivize the replacement of formerly in-residence-only training with distance training that enables Just-in-Time (JIT) learning. However, effective implementation of the MCA concept may also require adaptive training which considers the knowledge, skills, and attitudes (KSAs) developed by a trainee within their core career field when training them to perform activities which would typically be performed by individuals in another career field. This research presents a model-based systems engineering approach to support adaptive training in support of JIT for MCA by incorporating a task-operator analysis framework that aids the training requirements development process. This analysis seeks to facilitate training design guidelines that combine both traditional DoD and human system integration (HSI) instructional design methodologies. Data gained from the analysis seeks to identify the cognitive, affective, physical, and contextual training requirements at a level that fits what an airman who has been trained in a relative career field needs to learn given their existing KSAs.



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