Training Effectiveness of a Wide Area Virtual Environment in Medical Simulation

Author(s):  
Grady S. Wier ◽  
Rebekah Tree ◽  
Rasha Nusr
2014 ◽  
Vol 179 (1) ◽  
pp. 38-41 ◽  
Author(s):  
Craig Goolsby ◽  
Ryan Vest ◽  
Tress Goodwin

Author(s):  
Alan Liu ◽  
Eric Acosta ◽  
Jamie Cope ◽  
Valerie Henry ◽  
Fernando Reyes ◽  
...  

2017 ◽  
Vol 12 (1) ◽  
pp. 42 ◽  
Author(s):  
Roberto K. Champney ◽  
Kay M. Stanney ◽  
Laura Milham ◽  
Meredith B. Carroll ◽  
Joseph V. Cohn

2000 ◽  
Vol 9 (1) ◽  
pp. 52-68 ◽  
Author(s):  
Katherine L. Morse ◽  
Lubomir Bic ◽  
Michael Dillencourt

Large-scale distributed simulations model the activities of thousands of entities interacting in a virtual environment simulated over wide-area networks. Originally these systems used protocols that dictated that all entities broadcast messages about all activities, including remaining immobile or inactive, to all other entities, resulting in an explosion of incoming messages for all entities, most of which were of no interest. Using a filtering mechanism called interest management, some of these systems now allow entities to express interest in only the subset of information that is relevant to them. This paper surveys ten such systems, describing the purpose of the system, its scope, and the salient characteristics of its interest management scheme. We present the first taxonomy for such systems and classify the ten systems according to the taxonomy. The analysis of the classification reveals the fundamental nature of interest management and points to potential areas of research.


1998 ◽  
Vol 7 (2) ◽  
pp. 129-143 ◽  
Author(s):  
David Waller ◽  
Earl Hunt ◽  
David Knapp

Many training applications of virtual environments (VEs) require people to be able to transfer spatial knowledge acquired in a VE to a real-world situation. Using the concept of fidelity, we examine the variables that mediate the transfer of spatial knowledge and discuss the form and development of spatial representations in VE training. We report the results of an experiment in which groups were trained in six different environments (no training, real world, map, VE desktop, VE immersive, and VE long immersive) and then were asked to apply route and configurational knowledge in a real-world maze environment. Short periods of VE training were no more effective than map training; however with sufficient exposure to the virtual training environment, VE training eventually surpassed real-world training. Robust gender differences in training effectiveness of VEs were also found.


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