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A four-year research effort was conducted to collect empirical evidence on the effectiveness of different training methods for acquiring and transferring complex cognitive skills (see Plott et al., 2014). To accomplish this goal, a series of meta-analyses were conducted examining six training methods (training wheels, scaffolding, part-task training, increasing difficulty, learner control, and exploratory learning). Algorithms were then developed to quantify the relationships between the training methods, performance, and various moderating factors. These algorithms can be used to perform tradeoff analyses to determine the effectiveness of different combinations of training method(s), task/skill type(s) being trained (e.g., perceptual, psychomotor), trainee characteristics (e.g., experience, aptitude), and type(s) of training performance outcomes (e.g., learning, transfer). Finally, to ensure these research findings and algorithms would be easily consumable by training developers and researchers, a training effectiveness tool was developed, called TARGET (which stands for Training Aide: Research and Guidance for Effective Training). This tool can aid training developers and researchers in making decisions concerning the most appropriate training method(s) to use depending on their particular training context. This report focuses on the algorithm development completed as a part of this larger research effort, as well as the algorithm incorporation into TARGET.

This research effort was conducted to collect empirical evidence on the effectiveness of different training methods for acquiring and transferring complex cognitive skills. To accomplish this goal, we conducted a series of meta-analyses (and supplemental experiments) examining six training methods (training wheels, scaffolding, part-task training, increasing difficulty, learner control, and exploratory learning), as well as factors that moderate their effectiveness, such as task/skill type being trained (e.g., perceptual, psychomotor, cognitive-declarative), trainee characteristics (e.g., experience, aptitude), and type of training performance outcome (e.g., learning, transfer). Algorithms were developed to quantify the relationships between the training methods, performance, and the various moderating factors. These algorithms can be used to perform tradeoff analyses to determine the effectiveness of different combinations of training method(s), task/skill types, trainee characteristics, and performance outcomes. Finally, to ensure these research findings and algorithms would be easily consumable by training developers and researchers, a training effectiveness tool was developed, called TARGET (which stands for Training Aide: Research and Guidance for Effective Training). This tool can aid training developers and researchers in making evidence-based decisions concerning the most appropriate training method(s) to use depending on their particular training context; thus, helping to maximize effective learning and transfer.

This is a user guide for the web-based tool called Training Aide: Research and Guidance for Effective Training (TARGET). TARGET is a database of training effectiveness research findings that provides information on the relationships between training methods and performance. TARGET provides query-based searches of the training literature database, numerous visualizations of the relationships between training methods and performance, and it is updateable as additional training research is generated so that the tool stays current with state-of-the-art training research developments. It was designed to assist users in better understanding the relative effectiveness of different training methods for acquiring/transferring cognitive skills. The database includes six primary types of training methods (i.e., part task training, task difficulty sequencing, error prevention, scaffolding, learner control, and exploratory learning), as well as factors that moderate their effectiveness, such as skill type being trained, trainee characteristics (e.g., experience level), and type of training performance outcome (e.g., procedural knowledge, transfer). ARI Technical Report 1333 and ARI Research Note 2014-01 accompany this user guide.

This report was developed as part of a larger research project designed to identify evidence-based guidelines for the relative effectiveness of different training methods for acquiring and transferring skills involved in complex task domains. Broadly, the current report seeks to provide an update regarding the current state of the science on interpersonal skills (IPS) training. Specifically, this report discusses: 1) the literature review conducted for the broad area of IPS training, 2) literature surveys for six specific IPS with applicability to the U.S. Army, and 3) conclusions regarding the state of the science on IPS training. The broad IPS domain was covered by systematic search of interpersonal skills training research literature for the date range of 2000 through 2012. Skill-specific surveys of the literature covered active listening skill, assertive communication skill, nonverbal communication skill, relationship building skill, negotiation skill, and conflict resolution skill. The broad literature review conceptually frames the IPS domain. The six literature surveys serve as initial orientation to each IPS by providing: working definitions and alternative skill labels, a preliminary summary of the extant research, and a description of the experimental literature available for further detailed review and analysis.

The focus of this project was to apply research and modeling to inform future development of selection criteria and mechanisms for Army intelligence analysts. Our goals were to identify the cognitive attributes required for effective intelligence analysis and quantify the relationship of individual differences in those attributes and system-level performance. Our approach included two primary efforts: (1) a review of the scientific literature and research reports and (2) development of a task network model of the all-source intelligence analyst function. The outcomes of the literature review and modeling efforts are a set of actionable recommendations that could be taken to improve the performance of the Military Occupational Specialty 35F all-source intelligence analyst., Sponsored in part by U.S. Army Intelligence Center of Excellence, Fort Huachuca, AZ. The original document contains color images.

The impacts on performance of three different forms of communication (radio, chat, and tactile belt) were explored in the context of a small unmanned ground vehicle (SUGV) target identification task. The target identification task required a commander with knowledge of target locations and access to a digital map displaying the current SUGV position and orientation to direct a Soldier remotely operating the SUGV to the targets using a finite set of 11 commands. The study revealed no evidence of a loss of Soldier performance using the tactile belt communications channel. The finding suggests that the tactile use of haptic signals may be feasible, a potentially important finding for situations requiring covert communications., The original document contains color images.

The RC ATO (2004-2008), a joint program between the U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) and the Army Research Laboratory (ARL), has the mission of developing the appropriate tools, techniques and autonomy to maximize mounted and dismounted control of ground unmanned systems and optimize Soldier-robot and robot-robot teams. This was accomplished through the development and testing of assisted autonomy and situational awareness solutions, optimizing Soldier-machine interface usability across varying display sizes, control devices and dissimilar robotic systems and the deployment of unique multi-modal control techniques. The ATO concluded its Capstone Experiment and demonstration in September 2008. This paper will detail the technology developed and utilized under the program as well as highlight Capstone Experiment results., Presented at the AUVSI Conference on Unmanned Systems North America, held in Washington, DC, on 10-13 Aug 2009. Prepared in cooperation with Army Research Laboratory (ARL). Prepared in collaboration with DCS Corporation, and with Alion Science and Technology. The original document contains color images.