PrePARE

PrePARE

Research:

The difference between “trained” and “prepared” could save lives

It’s common for organizations to invest significantly in training and performance systems. But when push comes to shove, can they tell if these initiatives have truly prepared their people? PrePARE is designed to help the Air Force pull data from their systems to predict the readiness of their pararescue/combat rescue officers for future missions. From there, the Air Force can individually target which training is still needed (and more importantly, which isn’t).

Competency Management for First Responders

Project Details

Proposal Title:
PrePARE: Predictive Proficiency and Readiness Evaluation
Agency:
United States Air Force
Contract Numbers:
FA8650-13-M-6437, FA8650-16-C-6767
Start Dates:
2013, 2016

PrePARE is designed to be an interface between existing disparate systems to create a holistic end-to-end solution, mapping gathered performance data to ratings and requirements to ultimately create an optimized training and development plan. The work started as an effort by the Air Force to create a personalized, career-long approach to development that eliminates duplicate and extraneous training for those serving.

How we did it

A critical component to creating PrePARE is the creation of a performance data warehouse that gathers the data inputs from across the organization. In the first year of the project, we analyzed existing assessments and associated performance metrics and then defined standard data formats and a methodology for specifying performance metrics and evidence of competencies. From there, we could specify the metadata needed to enable the analysis and generate the predictive data. This allows a system-wide assessment of the various training systems which often include simulations, live training, and exercises of varying sizes and even game-based approaches.

The PrePARE model addresses the need for defining competencies with association proficiency levels for the skills involved. Training professionals can then map assessment performance results to competency proficiency levels, derive a predicted performance level based on accrued evidence, and then map a training remediation plan to best improve an individual’s competency gaps. This helps predict current readiness, as well as, the impact of integrated and joint training programs, and optimize future skill development for environments that involve multiple autonomous training and performance systems.