Assessing Engagement In Simulation-Based Training Systems For Virtual Kinesic Cue Detection Training

Combat Profiling techniques strengthen a Warfighter\u27s ability to quickly react to situations within the operational environment based upon observable behavioral identifiers. One significant domain-specific skill researched is kinesics, or the study of body language. A Warfighter\u27s ability to distinguish kinesic cues can greatly aid in the detection of possible threatening activities or individuals with harmful intent. This paper describes a research effort assessing the effectiveness of kinesic cue depiction within Simulation-Based Training (SBT) systems and the impact of engagement levels upon trainee performance. For this experiment, live training content served as the foundation for scenarios generated using Bohemia Interactive\u27s Virtual Battlespace 2 (VBS2). Training content was presented on a standard desktop computer or within a physically immersive Virtual Environment (VE). Results suggest that the utilization of a highly immersive VE is not critical to achieve optimal performance during familiarization training of kinesic cue detection. While there was not a significant difference in engagement between conditions, the data showed evidence to suggest decreased levels of engagement by participants using the immersive VE. Further analysis revealed that temporal dissociation, which was significantly lower in the immersive VE condition, was a predictor of simulation engagement. In one respect, this indicates that standard desktop systems are suited for transitioning existing kinesic familiarization training content from the classroom to a personal computer. However, interpretation of the results requires operational context that suggests the capabilities of high-fidelity immersive VEs are not fully utilized by existing training methodologies. Thus, this research serves as an illustration of technology advancements compelling the SBT community to evolve training methods in order to fully benefit from emerging technologies. © 2013 Springer-Verlag Berlin Heidelberg

Instructional Strategies for Scenario-Based Training of Human Behavior Cue Analysis with Robot-Aided Intelligence, Surveillance, Reconnaissance

The U.S. Army desires to improve safety during Intelligence, Surveillance, Reconnaissance (ISR) operations by removing Warfighters from direct line-of-fire by enhancing ISR operational capabilities with unmanned systems, also known as Robot-Aided ISR (RAISR) (DOD, 2013). Additionally, RAISR presents an opportunity to fulfill ISR capability requirements of modern combat environments including: detection of High-Value Individuals (HVI) from safer distances, identification of baseline behavior, and interpretation of adversarial intent (U.S. Army, 2008). Along with the demand and projected acquisition of RAISR technology, there is the added need to design training requirements for system operation and task execution instruction. While documentation identifying specific training standards and objectives for ISR tasks utilizing unmanned systems is limited (DOD, 2013), simulation-based training has been identified as a critical training medium for RAISR (U.S. Army, 2008). ISR analysts will primarily conduct RAISR tasks via Indirect Vision Displays (IVD) which transition well into multimodal simulations (Salcedo, Lackey, & Maraj, 2014). However, simulation alone may not fulfill the complex training needs of RAISR tasks, therefore, incorporating instructional support may improve the effectiveness of training (Oser, Gualtieri, Cannon-Bowers, & Salas, 1999). One method to accomplish this is to utilize a Scenario-Based Training (SBT) framework enhanced with instructional strategies to target specific training objectives. The purpose for the present experiment was to assess the effectiveness of SBT enhanced with selected instructional strategies for a PC-based RAISR training simulation. The specific task type was the identification of HVIs within a group through behavior cue analysis. The instructional strategies assessed in this experiment, Highlighting and Massed Exposure, have shown to improve attentional weighting, visual search, and pattern recognition skills, which are critical for successful behavior cue analysis. Training effectiveness was evaluated by analyzing the impact of the instructional strategies on performance outcomes, including detection accuracy, classification accuracy, and median response time, and perceptions of the level of engagement, immersion, and presence during training exercises. Performance results revealed that the Massed Exposure strategy produced significantly faster response times for one subtle and one familiar target behavior cue. Perception results indicated that Highlighting was the least challenging instructional strategy and the Control offered the preferred level of challenge. The relationships between performance and perception measures revealed that higher levels of engagement, immersion, and presence were associated with better performance in the Control, but this trend did not always hold for Massed Exposure and Highlighting. Furthermore, presence emerged as the primary predictor of performance for select target behavior cues in the Control and Massed Exposure conditions, while immersion and engagement predicted performance of select cues in the Highlighting condition. The findings of the present experiment point to the potential benefit of SBT instructional strategies to improve effectiveness of simulation-based training for behavior cue analysis during RAISR operations. Specifically, the findings suggest that the Massed Exposure strategy has the potential to improve response time when detecting both familiar and novel targets. The results also highlight directions for future research to investigate methods to alter instructional strategy design and delivery in order to improve trainee perceptions of the instruction

Investigating Simulation-Based Pattern Recognition Training For Behavior Cue Detection

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\u27s game. Kim\u27s game establishes patterns to identify inanimate objects, of which information retains in memory for later recall. The paper discusses the fundamental principles of Kim\u27s game applied to virtual Simulation-Based Training. The virtual version of Kim\u27s 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\u27s 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\u27s were conducted to examine the differences between Kim\u27s 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\u27s game and control strategies. Specifically, participants in the control condition performed better than the Kim\u27s game group on detection accuracy and response time. However, the Kim\u27s 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\u27s 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\u27s game and control group after the practice scenario. Next, a series of Spearman\u27s 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.