EAGER: Smart and Connected Communities: Engineering Networked Devices for Multiteam Learning and Performance

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This research project will apply interdisciplinary expertise to multiteam behavioral and learning research through the re-engineering of an innovative sociotechnical system to potentially transform research on multiteam systems in smart, connected communities. Multiteam systems are found in healthcare, military, disaster response, emergency response contexts,and many others, and can include the transfer of people, equipment, information, and coordination of cross-team interaction. One such example is the emergency response team transporting and handing over a patient to a hospital emergency department trauma team, however, multiteam behavioral tracking systems and the process of designing and developing them for smart, connected communities are applicable to other team-based contexts. Learning and coordination across multiple teams to prepare for an emergency or crisis response is crucial to prevent mistakes and to protect citizen health and safety. This research builds upon a proof of concept, mobile-based behavioral sensing system tested in real world conditions to connect learning, behavior and performance among multiple teams in a high fidelity simulation through establishing and modeling meaningful protocols toward improved sensor-based measurement, real-time data analysis and actionable insights for smart connected communities and the well-being of their citizens. This research will provide an unprecedented level of insight into multiteam behavior, transition points, and learning by participants. Processes to model and measure meaningful team-based interaction and activity will be addressed to identify metrics that matter in gauging multi-team performance. The researchers will study and leverage mobile behavioral sensing techniques and utilize sociotechnical analysis connected to real-time visualization techniques to aid multi-team learning. This research will link interdisciplinary perspectives in the learning sciences, behavioral sciences, engineering, data analytics and human factors to iteratively refine and test a system with potential to transform feedback and actionable insight for multiteam training contexts.