Keywords

Simulation based training, kim's game, virtual environment, instructional strategies

Abstract

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. ?

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2015

Semester

Spring

Advisor

Lackey, Stephanie

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Degree Program

Modeling and Simulation; Engineering

Format

application/pdf

Identifier

CFE0005659

URL

http://purl.fcla.edu/fcla/etd/CFE0005659

Language

English

Release Date

May 2018

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Open Access)

Download

Included in

Engineering Commons

Share

COinS