Predicting Requirements for instructional Environment Design to Improve Critical Training (PREDICT)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-07-M-0228
Agency Tracking Number: N071-099-0687
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2007
Solicitation Year: 2007
Solicitation Topic Code: N07-099
Solicitation Number: 2007.1
Small Business Information
12 Gill Street, Suite 1400, Woburn, MA, 01801
DUNS: 967259946
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jamie Estock
 Human Factors Scientist
 (202) 842-1548
Business Contact
 Margaret Clancy
Title: Chief Financial Officer
Phone: (781) 496-2415
Research Institution
Traditional military training is becoming increasingly constrained by limited resources, geographic distribution of personnel, and the nature of ongoing operations, leading to an increased interest in the use of virtual training environments. However, training developers and systems acquisition professionals need to better understand how the fidelity of sensory components of virtual environments impacts the effectiveness of training. The PREDICT effort proposes to develop a model-based tool to predict the impact of virtual training environment fidelity on training effectiveness. PREDICT will inform users, prior to investment, of the tradeoffs in training effectiveness associated with levels of fidelity. Aptima will base the PREDICT tool on: (1) a matrix that links virtual environment fidelity to training outcomes, (2) the training context variables that may influence the relationship between fidelity and training effectiveness, (3) the organization’s budgetary constraints, and (4) the costs associated with acquiring and maintaining virtual training environments. The result will be a complex, integrated, predictive model which considers all of these variables. In Phase II, Aptima will conduct model-based experimentation to validate the model’s predictions. To ensure success, the Aptima team will apply their knowledge of the fidelity literature with their expertise in predictive modeling, model-based experimentation, and experimental design.

* Information listed above is at the time of submission. *

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