Enhancing Military Trainings

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Introduction The focus of this paper is to build a theoretical framework that addresses my envisioned topic and its relevance to the research problem. The research topic stems from the problem U.S. Navy commander face when challenged to train and certify servicemembers in different warfare areas aboard ships while simultaneously achieving energy conservation as a derivative. Presently, Naval leaders are facing the same challenges of the past, where commanders have dealt with the difficulty of meeting mission essential tasks (MET) altogether amidst fiscal constrains imposed by risen costs of fossil fuel (Truly, 2001; Cane, 2008; Williams, 2009). Based on the budget challenges and current energy conservation initiatives, the quest for new technologies to support and enhance military training has taken off (Dombrowski, & Gholz, 2006; Cane, McCarthy & Halawi, 2010). For instance, according to Nicholson, Fiore, Vogel-Walcutt and Schatz (2009) “the military and industrial communities continue to design and develop scenario-based simulation systems, it seems prudent to investigate technologies and pedagogy that can improve their impact” (pg. 1932). In consequence, Fleet Synthetic Training (FST) was one of the initiatives that leveraged the use of blended training technologies to meet the training and certification requirements in an effort to mitigate the costs associated with warfare training and mission accomplishment (Williams, 2009; Cane et al, 2010; Cil, & Mala, 2010). Still, there is not a significant amount of literature available to validate the effectiveness of the pedagogies combined with the energy conservation initiatives. Accordingly, the emerging needs to rapidly train military service members in higher-order cogni... ... middle of paper ... ...the cooperation of multiple experts. Expert Systems with Applications, 34(4), 2826-2840. Chu, H. C., Hwang, G. J., Tsai, C. C., & Tseng, Judy C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 55(4), 1618-1627. Cil, I., & Mala, M. (2010). A multi-agent architecture for modeling and simulation of small military unit combat in asymmetric warfare. Expert Systems with Applications, 37(2), 1331-1343. Courtemanche, F., Najjar, M., & Mayers, A. (2008, January). Cognitive load estimation for optimizing learning within intelligent tutoring systems. Intelligent Tutoring Systems. 719-721. Davis, F. (1986), A technology acceptance model for empirically testing new end-user information systems: theory and results (Doctoral dissertation). MIT Sloan School of Management, Cambridge, MA.
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