TRAINING DECISION-INTENSIVE TASKS: A CONSTRUCTIVIST APPROACH

High technoloogy systems such as the airplane cockpit, the shipboard Combat Information Center (CIC), as well as nuclear power and other complex systems deamnd critical and effective decision making. This task environment is compex and ambiguous, decision makers must make sense of incompelte and often conflicting information, and the decision mker must respond to changing and often novel siturational demands and requirements. Yet, training often takes place in a very simplified classroom setting, decision makers must passibley learn principles and strategies that are applied to well-defined preoblems, in an environment that is quite different formt hte real-world setting in which this knowledge will have to be applied. The threat is that this training may reult in inert knowledge, information that the trainee has in memory, but does nto know how to use effectively in thre 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 pplications derived from this perspective.