Training emergency response decision makers using live, virtual and/or constructive simulations can be highly complex since certain situations can generate stimulusresponse cycles that depend significantly on unpredictable human judgments. In particular, effective training scenarios require a combination of content contributed via pre-authored scripts and content generated dynamically during the training exercise. Large-scale exercises require multiple domain experts contributing oversight and content to the scenario as it proceeds. Such real-time adaptation requires situational and group awareness based on an understanding of pre-scripted materials and the adaptations of others. This thesis describes the evolution and evaluation of a collaborative graphical timeline system, called the Scenario Timeline System (STS), which facilitates asynchronous and synchronous collaborative timeline management, and its application in large-scale, computer-supported emergency response training exercises.
Training emergency response decision makers using live, virtual and/or constructive simulations can be highly complex since certain situations can generate stimulusresponse cycles that depend significantly on unpredictable human judgments. In particular, effective training scenarios require a combination of content contributed via pre-authored scripts and content generated dynamically during the training exercise. Large-scale exercises require multiple domain experts contributing oversight and content to the scenario as it proceeds. Such real-time adaptation requires situational and group awareness based on an understanding of pre-scripted materials and the adaptations of others. This thesis describes the evolution and evaluation of a collaborative graphical timeline system, called the Scenario Timeline System (STS), which facilitates asynchronous and synchronous collaborative timeline management, and its application in large-scale, computer-supported emergency response training exercises.
