MCMSim was developed using Blender v a popular and free

MCMSim was developed using Blender (v2.49), a popular and free open source 3D modelling and animation tool and game engine. Blender\'s interface simultaneously supports concurrent 3D modelling and simulation/game development and, as such, suits the rapid development and evaluation of proof-of-concept applications such as that described here. The Blender Game Engine utilises the Bullet real-time physics engine for rigid and soft body simulation and collision detection. The embedded Python (v7) language interpreter supports the definition of game logic using visual programming blocks and scripting. Python affords the use of bindings (modules) to add custom functionality; this is essential in MCMSim because of the requirement for AAR functions involving data storage and parsing, and specific control mechanisms related to the use of multiple input devices.
MCMSim AAR – pilot study experiment
Discussion & conclusions There are three central themes that seem to reoccur in much of the (limited) reported work in this field. These are (a) appreciation on the part of the user as to the extent to which their own performance was successful, (b) specifically understanding which parts of a task performance were successful and unsuccessful and their impact upon the final outcome, and (c) how to construct new strategies from expert knowledge in order to perform better in the future. Such themes are often central to theories related to cognitive processes of learning, one in particular being the constructionist approach to learning (e.g. Reference [34]), which argues that knowledge acquisition is a process of continuous self-construction, based on interactions between personal experiences and ideas. Central to the effectiveness of such a ionomycin is the ability to formulate plans and test their outcomes; it is this formulation of plans where AAR appears to be capable of enhancing the performance of individuals in virtual environments. Peachley [35] also highlights the importance of constructionist approach to learning in virtual environments, stressing the potential of VEs to be a test bed for the formulation of more effective strategies for the real world, as is the ultimate aim of the work presented in the present paper. By analysing the quantitative effects that AAR has on performance in a simulation, the results presented here have taken a small step forward from previously reported work, especially given the fact that subjective results have dominated most studies of AAR to date. AAR attempts to address the often sporadic nature of strategy formulation in such environments, by providing the user with knowledge of success or failure evident in previous performances. Combining Signal sequence with expert knowledge allows the user to formulate strategies that are far more effective for subsequent performances and may even help to guarantee a strong positive transfer of knowledge and skills from the virtual to the real and to minimise skill fade over time. Whilst the research reported in the present paper only took the form of a pilot study, the impact of the results has been quite significant and has supported the further development of underwater scenarios for both visualisation and training purposes. Since the execution of the work described here, there have been numerous developments in the capabilities of games engines in supporting credible underwater scenarios, with physics engines supporting high-fidelity scenes featuring, for example, very fine particles, in both suspended and dynamic states, accurate underwater fogging effects and the ability to simulate backscatter from diver-held or vehicle-mounted light sources. The ability to embed software capable of capturing key elements of end user behaviours supporting real-time AARs – motion paths, dwell points, object interactions and own-view camera angles (i.e. viewing frustum) – has also been taken further in other defence and healthcare projects, for assessing the impact of interface technologies on end users\' navigation and interaction strategies. These include the development of a deployment activity recording system for the UK\'s CUTLASS bomb disposal telerobot VR trainer [9] and a “tracking and behaviour capture system” for assessing patient interactions with large-scale virtual restorative environments deployed in hospitals (e.g [36]).