1 Improved Performance Research Integration Tool Training Algorithm Enhancements Alion Microanalysis & Design Individual Operator Study IMPRINT, the Improved Performance Research Integration Tool, is used to model the effects of manpower and workload issues on human performance. This project augmented the ability of IMPRINT to predict training effects. Alion built an IMPRINT Pro Advanced Training Effects plug-in that allows an operator to model training strategies and how those strategies impact both skill acquisition and performance after decay. Experiments were conducted to collect empirical performance data on two realistic military tasks: flying a Predator Unmanned Aerial System in a Synthetic Task Environment and controlling a simulated autonomous ground vehicle. The data were analyzed at the taxon level, an integrated human performance taxonomy already contained in IMPRINT. Taxons allowed us to generalize training effects to different types of tasks. Alion built a computational model of the Predator Unmanned Aerial System domain to demonstrate training effects in action and context. In addition, we studied improvement to the maintenance modeling capabilities in IMPRINT Pro. 2 Science Applications International Corporation Individual Operator Study For several decades, computer-based modeling and simulation tools (e.g., IMPRINT) have been used to support the systems acquisition process. Historically, the emphasis applied to simulation-based acquisition activities has focused on system capabilities that do not include the process of training operators that are an integral part of the system. The Air Force Research Laboratory’s Warfighter Readiness Research Division is sponsoring a number of studies aimed at quantifying the relationship between training strategies and operator performance. In this study, sixty university students participated in a training program to learn five tasks accomplished by unmanned aircraft system sensor operators. The training program followed one of three strategies that varied interactivity between the student and instructor. Data were collected during two simulator sessions, the first during initial training and the second after a retention interval. The data were analyzed to determine effects of training strategy, retention interval, and nature of the task learned on performance measured by time to complete the task and the accuracy to which it was performed. These data were fitted to models to predict rates of skill acquisition, retention, and reacquisition, and were implemented in an IMPRINT model. Model development and validation processes are presented, and recommendations are discussed. Science Applications International Corporation Team Training Study An experiment was conducted to quantify the effects of teamwork training on mission performance and team processes. A baseline training condition (no formal teamwork training) and three teamwork training strategies were examined. These included Communication skills training, Supportive skills training, and Corrective skills training. Differences between teams’ pre-test (pre-team training) and post-test (post-team training) performance and process scores served as a measure of the effect of teamwork training. Trends in the data indicated that teamwork training enhanced both team performance and teamwork processes, with task time being affected more than accuracy. A high degree of variability in the data limited the statistical significance of the teamwork training strategy effects. Implications of the findings are discussed and an approach to implementing teamwork training effects in the IMPRINT modeling environment is offered. 3 JXT Applications Maintenance Training Study The goal of this study was to make the training forecasting capability of IMPRINT more robust. The effort aimed to increase IMPRINT’s capability to forecast performance gains for maintenance tasks depending on the instructional strategy used. The goal was to be achieved by conducting empirical studies to estimate the impact of instructional strategies on learning and forgetting in the maintenance domain. Working with automotive program students at Florida Community College Jacksonville and general university students at Florida State University, the research team trained the subjects using computer based training, and observed and recorded the subjects’ performance on five maintenance tasks. Based on the results of the descriptive statistics, Accuracy lacked variance. Specifically, subjects exhibited performance at nearly 100% across all conditions. While this study of Air Force maintenance training is reflective of current practices, the Air Force has indicated that it is changing the way it does business. If the Air Force moves training into a collaborative world where best practices are shared across time and space, then this new environment will have implications for Air Force maintenance training. This is a different picture than the one reflected by the data examined in this report. Florida State University Literature Review & Meta Analysis This technical report describes the major activities and results from a contract between the United States Air Force Research Lab and the Florida State University Learning Systems Institute. The general project goal was to employ a model-based approach for aligning instructional strategies with technical task performance. The modeling system used in this effort was the Improved Performance Research Integration Tool (IMPRINT). IMPRINT has been used successfully by the United States Military to predict human performance in complex and dynamic operational environments. At the outset of this project, however, IMPRINT did not include a training component to determine the effects of instructional approaches on task performance within various learning taxonomic domains. In order to achieve the project goal, the project team carried out an extensive literature review on the effects of training on technical task performance and developed a training effects algorithm based on the meta-analysis of relevant studies. The training effects algorithm acts as a plug-in to the IMPRINT system and has been shown to effectively model training effects in a technical mission.
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