SIDH: A Game-Based Architecture for a Training Simulator

Game-based simulators, sometimes referred to as “lightweight” simulators, have benefits such as flexible technology and economic feasibility. In this article, we extend the notion of a game-based simulator by introducing multiple screen view and physical interaction. These features are expected to enhance immersion and fidelity. By utilizing these concepts we have constructed a training simulator for breathing apparatus entry. Game hardware and software have been used to produce the application. More important, the application itself is deliberately designed to be a game. Indeed, one important design goal is to create an entertaining and motivating experience combined with learning goals in order to create a serious game. The system has been evaluated in cooperation with the Swedish Rescue Services Agency to see which architectural features contribute to perceived fidelity. The modes of visualization and interaction as well as level design contribute to the usefulness of the system

Enhanced Training through Interactive Visualization of Training Objectives and Models

Military forces operate in complex and dynamic environments [1] where bad decisions might have fatal consequences. A key ability of the commander, team and individual warfighter is to quickly adapt to novel situations. Live, Virtual and Constructive training environments all provide elements of best practices for this type of training. However, many of the virtual training are designed without thorough consideration of the effectiveness and efficiency of embedded instructional strategies [2], and without considering the cognitive capabilities and limitations of trainees. As highlighted recently by Stacy and Freeman [3], large military training exercises require a significant commitment of resources, and to net a return on that investment, training scenarios for these events should systematically address well-specified training objectives, even if they often, do not. In order to overcome these shortcomings with both Live and Virtual training systems and following our previous work [4,5,6], this paper presents a design solution for a proof-of-concept prototype that visualizes and manages training objectives and performance measures, at individual and collective levels. To illustrate its functionality we use real-world data from Live training exercises. Finally, this paper discusses how to learn from previous training experiences using data mining methods in order to build training models to provide instructional personalized feedback to trainees.NOVA 20140294 (Knowledge Foundation

Enhanced Training through Interactive Visualization of Training Objectives and Models

Military forces operate in complex and dynamic environments [1] where bad decisions might have fatal consequences. A key ability of the commander, team and individual warfighter is to quickly adapt to novel situations. Live, Virtual and Constructive training environments all provide elements of best practices for this type of training. However, many of the virtual training are designed without thorough consideration of the effectiveness and efficiency of embedded instructional strategies [2], and without considering the cognitive capabilities and limitations of trainees. As highlighted recently by Stacy and Freeman [3], large military training exercises require a significant commitment of resources, and to net a return on that investment, training scenarios for these events should systematically address well-specified training objectives, even if they often, do not. In order to overcome these shortcomings with both Live and Virtual training systems and following our previous work [4,5,6], this paper presents a design solution for a proof-of-concept prototype that visualizes and manages training objectives and performance measures, at individual and collective levels. To illustrate its functionality we use real-world data from Live training exercises. Finally, this paper discusses how to learn from previous training experiences using data mining methods in order to build training models to provide instructional personalized feedback to trainees.NOVA 20140294 (Knowledge Foundation