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- W2783237103 abstract "It is standard practice worldwide to train based on one-time task checklists and fixed calendar-based refresher training intervals. These antiquated approaches make invalid assumptions about the dynamics of human learning and memory, ignoring most of what we have learned on those topics in the last century. These are risky, costly, and frustrating practices that should be discarded in favor of new methods informed by state of the art cognitive science and technology. At the U.S. Air Force Research Laboratory we have been pushing the boundaries of cognitive science and technology to improve our ability to make evidence-based predictions of human performance. This resulted in a new patented technology called the Predictive Performance Optimizer (PPO). A key application of this technology is in the scheduling of learning events, for instance to acquire initial proficiency, to maximize retention, or to enhance relearning and maintain proficiency at a minimum required level. In this presentation I will describe key phenomena from the relevant empirical literature, criteria for the application of models of learning and memory, and initial results from a field study evaluating the use of PPO in training cardiopulmonary resuscitation (CPR), basic lifesaving skills relevant to military operations and more generally to medical and civilian communities worldwide." @default.
- W2783237103 created "2018-01-26" @default.
- W2783237103 creator A5072465067 @default.
- W2783237103 date "2017-11-01" @default.
- W2783237103 modified "2023-09-29" @default.
- W2783237103 title "Plenary talk: Human performance prediction: Basic mechanisms and exploratory applications" @default.
- W2783237103 doi "https://doi.org/10.1109/acdtj.2017.8259583" @default.
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