Modeling Training Effects Using a Human Performance Taxonomy

Abstract

Objective: The aim of this study was to characterize skill acquisition during training and skill retention as a function of training strategy, retention period, and task type in the form of a numerical model and then apply that model to make predictions of performance on an unknown task.

Background: Complex systems require efficient and effective training programs for the humans who operate them in discontinuous fashion. Although there are several constructs for learning theory, models that enable analysts to predict training outcomes are needed during the design of training programs.

Method: This study involved 60 participants who were trained on five tasks relevant to RQ-1 Predator unmanned aircraft system sensor operators by one of three strategies that represented a continuum of instructor interactivity. After training, performance data for all five tasks were collected. Participants completed the same tasks 30 or 60 days later to determine skill retention and the rate at which task proficiency was reacquired.

Results: Models built from tasks that isolate human performance channels adequately predicted performance on a task that combined those channels.

Conclusion: Models that predict performance on tasks that isolate human performance channels can be used to make predictions on tasks that draw on multiple channels. This model provided a distribution of performance data that was statistically similar to actual performance data.

Application: System designers trained with human performance data on a set of tasks can apply those tasks’ characteristics to future tasks to make reasonably accurate performance predictions, thereby allowing the designers to make early decisions regarding training strategy to teach those tasks.