Model-based Optimal System for Training (MOST)

Award Information
Agency:
Department of Defense
Amount:
$396,331.00
Program:
STTR
Contract:
FA9550-05-C-0101
Solitcitation Year:
2004
Solicitation Number:
N/A
Branch:
Air Force
Award Year:
2005
Phase:
Phase II
Agency Tracking Number:
F045-014-0218
Solicitation Topic Code:
AF04-T014
Small Business Information
APTIMA, INC.
12 Gill Street, Suite 1400, Woburn, MA, 01801
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
967259946
Principal Investigator
 Jared Freeman
 Vice President, Research
 (202) 842-1548
 freeman@aptima.com
Business Contact
 Margaret Clancy
Title: Chief Financial Officer
Phone: (781) 496-2415
Email: clancy@aptima.com
Research Institution
 WRIGHT STATE UNIV.
 William K Sellers
 Office of Research & Programs, 3640 Colonel Glenn
Dayton, OH 45435, OH, 05435
 (937) 775-2664
 Nonprofit college or university
Abstract
The Model-based Optimal System for Training (MOST) is an optimal Intelligent Team Tutoring System (ITTS) for training team and individual resource allocation skills. MOST will contain five models; (1) an optimal task performance model (expert model), (2) an optimal context model, (3) an optimal feedback model, (4) an optimal procedural training model, and (5) a student model. The optimal context, feedback, and procedural training models constitute the didactic model. These three sub-models, coupled with a hierarchical task decomposition, will allow the training to be optimized on three vectors; (1) the environment or context, (2) feedback, guided practice, and modeling to develop strategic knowledge, and (3) feedback and practice to develop procedural knowledge and skills. Existing air planning and control task analyses and archival data from hundreds of experiment participants will be analyzed to create the hierarchical task, optimal task performance, and didactic models. In operation, the ITTS will probe and monitor individual and team performance levels, provide feedback, procedural training, and/or modifications to the training context to build on the trainees’ current skill level. In Phase I, we initiated the optimal task performance model and optimal strategy modeling feedback for resource allocation teams in an After Action Review (AAR).

* information listed above is at the time of submission.

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