CASE STUDIES

Predicting the Impact of Simulator Fidelity on Training Effectiveness for the United States Air Force

PERFORM

The Problem

The United States Air Force was interested in a standard approach and tool for matching training objectives to the training device with the most appropriate fidelity — from lower-fidelity simulators, to higher-fidelity simulators, to actual training in the aircraft. Employing the proper level of fidelity would ensure better training results and reduce the cost by eliminating investments in unnecessary training and technology.

The Solution

To address this need, the Air Force Research Laboratory (AFRL) contracted Aptima to develop a model-based decision-support tool that would predict the impact of simulator fidelity on training effectiveness. The tool is called PERFORM — Performance Effects Related to FORce-cueing Manipulation. Here is how Aptima, working in close coordination with AFRL, delivered the solution:

The RELATE Approach – Aptima developed a six-step approach called RELATE — Relating Effective Learning to Attributes of the Training Environment — to establish quantitative, predictive relationships between simulator fidelity and training effectiveness. RELATE fuses fidelity requirements defined by end-users, existing theory and research about fidelity, and objective performance data from fidelity experiments to develop a predictive, computational model.

Fidelity Experiments – Aptima conducted fidelity experiments to refine and validate the model. Aptima collected training effectiveness data in studies conducted at AFRL in Mesa, AZ involving F-16 pilots flying air-to-air training research missions. Aptima compared pre- and post-training performance between pilots who trained in two simulators at differing levels of fidelity.

The PERFORM Tool – Aptima built PERFORM on the model developed using the RELATE approach and refined during the fidelity experiments. PERFORM allows users to predict the effectiveness of a given training simulator to meet specific objectives. Users input the fidelity of simulators and compare the predicted training effectiveness of each simulator side-by-side. PERFORM provides a transparent view of the model, which gives users a high level of understanding and confidence in the output.

The Results

PERFORM gives users the ability to make more informed simulator development and acquisition decisions, and provides guidance on how to employ a suite of high- and low fidelity simulators for the greatest training effect. PERFORM provides accurate predictions of the effectiveness of training simulators by integrating data from end-users, theory and research, and experimentation in a computational model. PERFORM can help users:

Prioritize technology enhancements to improve the training effectiveness of existing simulators;

Determine what simulator to develop or acquire; and

Develop an integrated strategy for employing both high- and low-fidelity simulators to meet training objectives.