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Military » Damage Control

Advanced Technology for Automated Performance Assessment, Office of Naval Research

In a multi-year research project funded by the Office of Naval Research, CRESST developed reliable and valid methods to automatically assess cognitively complex technical tasks undertaken by trainees while using games and simulations. The focus of the research was the development of a conceptual framework for automated performance assessment that can be generalized to a variety of domains. The goal is to provide a generic model of assessment that can be incorporated into both new and preexisting computer-based simulations that depict cognitively complex scenarios.



Outcomes
The outcome for the project was to provide a testbed for research on automated performance assessment, CRESST developed a 3-D, computer-based simulation depicting the interior of a Navy ship. Assuming command of a damage control repair locker, the player is tasked with identifying and responding to a variety of flood and fire casualties. Using a dynamic Bayesian network, all actions and decisions related to situation awareness and decision making are evaluated and recorded in real time and are used for both formative and summative assessments of performance.

Related Publications
Koenig, A. D., Lee, J., Iseli, M.., & Wainess, R. C. (2009, December). A conceptual framework for assessing performance in games and simulations. Proceedings of the Interservice/Industry Training, Simulation and Education Conference , Orlando, FL. Download here

Iseli, M.., Koenig, A. D., Lee, J., ↦ Wainess, R. C. (2010, December). Automatic assessment of complex task performance in games and simulations. Proceedings of the Interservice/Industry Training, Simulation and Education Conference, Orlando, FL. Download here

For more information please contact:
Alan Koenig, Ph.D., Senior Researcher
Phone: 310-825-4124
Email: koenig@cse.ucla.edu

Training Models and Tools for Adaptive Learning, Office of Naval Research

CRESST, in partnership with the U. S. Navy Surface Warfare Officers School Command (SWOS), conducted an analysis of the LCS (Littoral Combat Ship) Readiness Control Officer (RCO) portion of the SWOS Prospective Engineering Officer (PEO) course.



The goal was to determine the overall learning stage required to become a competent RCO watchstander. Learning stages were defined as Cognitive, Associative, and Autonomous, with Cognitive indicative of memorization and recall, Associative indicative of automating some knowledge and skills but relying heavily on checklists and other documentation, and Autonomous indicative of expert performance where both routine and novel situations are assessed and responded to with little or no conscious thought.

Outcomes
CRESST analyzed all Terminal Learning Objectives (TLOs), Enabling Learning Objectives (ELOs), and Performance Standards to determine their required learning stages, providing documentation to support its determinations, and definitions and examples of the three learning stages as a rubric. We determined the overall learning stage requirement for the course, as well as the individual learning stage requirement for each TLO and ELO.

Publications
Wainess, R. Final Report: RCO Course Analysis. October 11, 2011.

For more information please contact:
Richard Wainess, Ph.D., Senior Researcher
Phone: 310-206-5561
Email: wainess@cse.ucla.edu