Personalizing training to acquire and sustain competence through use of a cognitive model
One-size-fits-all fixed calendar date approaches to training have proven to be inadequate across an array of domains and contexts, and in the medical field specifically, many studies document that skills deteriorate as early as two months after training (e.g., Madden 2006; Woollard et al. 2006). Given the individual differences learners inherently possess, we posit that it would be much more prudent to personalize training around individual learner needs, so that competency could be both attained and sustained in a tailored and streamlined fashion. We hypothesize that through the application of an innovative new cognitive technology, known as the Predictive Performance Optimizer (PPO), individual trainees may reduce unnecessary time in training while increasing performance effectiveness compared to learners given similar training opportunities at fixed times. PPO functions by capitalizing on the fidelity of objective performance data captured through simulation to prescribe training events/refreshers that help individuals both acquire and sustain competency in specific skills, including CPR, trauma assessment, laparoscopic surgery, and intracranial pressure monitoring. We have amassed increased levels of evidence revealing the ability of PPO to personalize training and prescribe tailored, customized regimens designed to help trainees both acquire and sustain competencies both efficiently and effectively.
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- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Start / End Page
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences