Simulation based training, kim's game, virtual environment, instructional strategies
The U.S. military uses pattern recognition training to observe anomalies in human behavior. An examination of the pattern recognition training literature for Warfighters reveals a gap in training to discern patterns of human behavior in live environments. Additionally, the current state of warfare is evolving and requires operations to change. As a result, pattern recognition training must accommodate new practices to improve performance. A technique used to improve memory for identifying patterns in the environment is Kim's game. Kim's game establishes patterns to identify inanimate objects, of which information retains in memory for later recall. The paper discusses the fundamental principles of Kim's game applied to virtual Simulation-Based Training. The virtual version of Kim's game contains customized scenarios for training behavior cue analysis. Virtual agents display kinesic cues that exhibit aggressive (i.e., slap hands and clench fist) and nervous behaviors including wring hands and check six. This research takes a novel approach by animating the kinesics cues in the virtual version of Kim's game for pattern recognition training. Detection accuracy, response time, and false positive detection serve as the performance data for analysis. Additional survey data collected include engagement, flow, and simulator sickness. All collected data was compared to a control condition to examine its effectiveness of behavior cue detection. A series of one-way between subjects design ANOVA's were conducted to examine the differences between Kim's game and control on post-test performance. Although, the results from this experiment showed no significance in post-test performance, the percent change in post-test performance provide further insight into the results of the Kim's game and control strategies. Specifically, participants in the control condition performed better than the Kim's game group on detection accuracy and response time. However, the Kim's game group outperformed the control group on false positive detection. Further, this experiment explored the differences in Engagement, Flow, and Simulator Sickness after the practice scenario between Kim's game group and the control group. The results found no significant difference in Engagement, partial significance for Flow, and significant difference for Simulator Sickness between the Kim's game and control group after the practice scenario. Next, a series of Spearman's rank correlations were conducted to assess the relationships between Engagement, Flow, Simulator Sickness, and post-test performance, as well as examine the relationship between working memory and training performance; resulting in meaningful correlations to explain the relationships and identifying new concepts to explain unrelated variables. Finally, the role of Engagement, Flow, and Simulator Sickness as a predictor of post-test performance was examined using a series of multiple linear regressions. The results highlighted Simulator Sickness as a significant predictor of post-test performance. Overall, the results from this experiment proposes to expand the body of pattern recognition training literature by identifying strategies that enhance behavior cue detection training. Furthermore, it provides recommendations to training and education communities for improving behavior cue analysis. ?
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Modeling and Simulation; Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Maraj, Crystal, "Investigating Simulation-Based Pattern Recognition Training For Behavior Cue Detection" (2015). Electronic Theses and Dissertations, 2004-2019. 1285.