High technology systems such as the airplane cockpit, the shipboard Combat Information Center (CIC), as well as nuclear power and other complex systems demand critical and effective decision making. This task environment is complex and ambiguous, decision makers must make sense of incomplete and often conflicting information, and the decision maker must respond to changing and often novel situational demands and requirements. Yet, training often takes place in a very simplified classroom setting, decision makers must passably learn principles and strategies that are applied to well-defined problems, in an environment that is quite different format the real-world setting in which this knowledge will have to be applied. The threat is that this training may result in inert knowledge, information that the trainee has in memory, but does not know how to use effectively in the real-world setting. Constructivist learning theories present one approach to reduce the gap between knowing information and knowing how to use information in complex task environments. The goal of the research described in this proposal is to evaluate the applicability of constuctivist approaches to training decision-intensive tasks, and to conduct empirical research to test training principles and applications derived from this perspective.
