Robert A. Sottilare

Automated scenario generation: toward tailored and optimized military training in virtual environments

Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper, we enumerate the advantages of incorporating automated scenario generation technologies into the traditional scenario development pipeline. An automated scenario generator is a system that creates training scenarios from scratch, augmenting human authoring to rapidly develop new scenarios, providing a richer diversity of tailored training opportunities, and delivering training scenarios on demand. We introduce a combinatorial optimization approach to scenario generation to deliver the requisite diversity and quality of scenarios while tailoring the scenarios to a particular learners needs and abilities. We propose a set of evaluation metrics appropriate to scenario generation technologies and present preliminary evidence for the suitability of our approach compared to other scenario generation approaches.

Predicting learner engagement during well-defined and Ill-defined computer-based intercultural interactions

This article reviews the first of two experiments investigating the effect tailoring of training content has on a learners perceived engagement, and to examine the influence the Big Five Personality Test and the Self-Assessment Manikin (SAM) mood dimensions have on these outcome measures. A secondary objective is to then correlate signals from physiological sensors and other variables of interest, and to develop a model of learner engagement. Self-reported measures were derived from the engagement index of the Independent Television Commission-Sense of Presence Inventory (ITC-SOPI). Physiological measures were based on the commercial Emotiv Epoc Electroencephalograph (EEG) braincomputer interface. Analysis shows personality factors to be reliable predictors of general engagement within well-defined and ill-defined tasks, and could be used to tailor instructional strategies where engagement was predicted to be nonoptimal. It was also evident that Emotiv provides reliable measures of engagement and excitement in near real-time.

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams.

Shared Mental Models in Support of Adaptive Instruction for Teams Using the GIFT Tutoring Architecture.

Teams and teamwork are ubiquitous in military and civilian organizations. Their importance to organizational success cannot be overstated. This article describes the relationship and effect of three concepts: Intelligent Tutoring Systems (ITSs), shared mental models, and teamwork. The nexus between these concepts is examined to determine its capability to support adaptive instruction of teams, defined here as collectives of interdependent individuals who must communicate and interact with each other in order to perform assigned tasks and missions. An assumption underlying this examination is that augmenting the mental modeling processes of ITS with the mental models shared by members of interdependent teams will allow the considerable and increasingly research-established capabilities of intelligent tutoring of individuals to be applied in training teams. Specifically, we reviewed the learning and performance literature to identify how shared mental models of cognition could be used to enhance the adaptive instruction of teams. Our goal is to develop a methodology to enhance training and educational options for institutions that provide adaptive team instruction at the point-of-need. Toward this end, we discuss the adaptation of the Generalized Intelligent Framework for Tutoring (GIFT), an open source tutoring architecture, to accommodate team models and states. While this article makes a first step toward defining a process for team tutoring, challenges remain. Team tutors must have the ability to manage uncertainty and the dynamic nature of team interaction and communication in order to make effective and timely decisions that optimize team and team member performance.

Visual modality research in virtual and mixed reality simulation

Military organizations worldwide continue to invest heavily in research, development, and fielding of virtual and mixed reality simulations and simulators for training. However, the future fiscal environment will be challenging for both simulation as well as the US Army as a whole. Thus, wise design decisions must be made when developing virtual simulations for training. In order to optimize the effectiveness of these simulations, developers must employ trade-off analysis and scientific methods to derive empirical evidence, in order to ensure that the simulation under development is optimized to meet the training requirements, while still adhering to cost and schedule constraints. This paper specifically focuses on the task of employing the optimal visual modality in virtual and mixed reality simulations. This paper reviews the literature on training simulation benefits and taxonomy, and examines the training efficacy of virtual and mixed reality simulation. Major concepts and applications of virtual and mixed reality simulation training efficacy are discussed. A key component of virtual simulation, visual modality, is examined through a literature review and recommendations for visual display design parameters are provided.

Adaptive instruction for medical training in the psychomotor domain

The adaptive instruction provided by Intelligent Tutoring Systems (ITSs) tailors direction, support, and feedback to enhance/maintain the learning needs (e.g., lack of knowledge or skill) of each individual. Today, ITSs are generally developed to support desktop training applications, with the most common domains involving cognitive problem solving tasks (e.g., mathematics and physics). In recent years, implementations of game-based tutors authored using the Generalized Intelligent Framework for Tutoring (GIFT), an open-source tutoring architecture, provided tailored training experiences for military tasks through desktop applications (e.g., games including Virtual Battlespace and Virtual Medic). However, these game-based desktop tutors have also been limited to adaptive instruction for cognitive tasks (e.g., problem solving and decision-making). The military requires adaptive instruction to extend beyond the desktop to be compatible with the physical nature of many tasks performed by soldiers, sailors, and airmen. This article examines how commercial sensor technologies might be adapted to work with GIFT and support tailored computer-guided instruction in the psychomotor domain for a military medical training task, specifically hemorrhage control. Toward this goal, we evaluated the usability and system features of commercial smart glasses and pressure-sensing technologies. Smart glasses were selected as the focus of this study over handheld mobile devices in order to promote a hands-free experience during the training of hemorrhage-control tasks on a mannequin. Pressure sensors were selected to provide direct measures of effectiveness during the application of tourniquets and pressure bandages. Each set of technologies (smart glasses and pressure sensors) was evaluated not with respect to each other, but with respect to their capabilities to support adaptive instruction in the wild at the learner’s point-of-need and criteria based on established usability heuristics. Instruction in the wild is training provided in an environment outside the classroom and areas where tracking and sensing infrastructure are available (e.g., deployed areas of operation). We examined a wide range of features and capabilities, and evaluated their compatibility with the hemorrhage-control task, to answer the following question: what system design features (e.g., usability and interaction) are needed to support adaptive instruction for this individual psychomotor task at the point-of-need in locations where no formal training infrastructure is available?

Using Learner Data to Influence Performance during Adaptive Tutoring Experiences

During computer-based tutoring sessions, Intelligent Tutoring Systems (ITSs) adapt planning and manage real-time instructional decisions. The link between learner data and enhanced performance is the adaptive tutoring learning effect chain through which learner data informs learner state classification which in turn informs optimal instructional decisions to enhance performance. This paper examines the roles and influence of learner data in both short-term (also called run-time or session) and long-term (also called persistent) learner models used to support adaptive tutoring decisions within the Generalized Intelligent Framework for Tutoring (GIFT). To enhance the usability of tutoring systems and learner performance, recommendations for the design of future learner models are also presented.

Injecting Realistic Human Models into the Optical Display of A Future Land Warrior System for Embedded Training Purposes

Head Mounted Displays (HMDs) have traditionally been and are currently utilized in a variety of applications including training, entertainment, and military operations. Most applications of HMDs seek to provide the user with enriched information, that is, information beyond that of what a human can gather with only biological vision. The Land Warrior is an operational system that utilizes a HMD. For Land Warrior, the HMD provides the Soldier better battle field situational understanding through text by communicating command and control information. Other Land Warrior battlefield tasks include target detection and recognition. This paper evaluates the visual components of the current Ground Soldier System (GSS), Land Warrior HMD, analyzes embedded training requirements (specifically detection ranges for embedded virtual targets and human performance issues associated with HMDs), and proposes an optical model to overcome some of the limitations inherent to the current commercial version of the Land Warrior HMD and other commercially-available HMDs. The design quality of the Land Warrior HMD has a direct impact on a soldiers performance in the field. The varying stringent environment in which LW operates provides HMD visual component design challenges, due to the correlation between the equipment style, information formatting, and use environment. Information formatting affects a Soldiers workload. The current LW HMD visual implementation contains information formatting limitations based on its ability to scale to other resolutions and size linearly. By increasing the performance of the HMD of the Land Warrior System, the potential to improve readiness and survivability also increase. Forty-five thousand sets of Land Warrior equipment will be fielded between 2001 and 2014.

Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research

The Generalized Intelligent Framework for Tutoring (GIFT) is a research prototype with three general goals associated with its functions and components: 1) lower the skills and time required to author Intelligent Tutoring Systems (ITSs) in a variety of task domains; 2) provide effective adaptive instruction tailored to the needs of each individual learner or team of learners; and 3) provide tools and methods to evaluate the effectiveness of ITSs and support research to continuously improve instructional best practices. This special issue focuses primarily on the third goal, GIFT as a research testbed. A discussion thread covers each article within this special issue and discusses its actual and potential impact on GIFT as a research tool for AIED. Our primary motivation was to introduce the AIED community to GIFT not just as a research tool, but as an extension of familiar challenges taken on previously by AIED scientists and practitioners. This preface provides a high level overview of the GIFT functions (authoring, instructional delivery and management, and experimentation) and presents its primary design principles. To learn more about GIFT, freely access the software, documentation, and associated technical papers visit www.GIFTtutoring.org.

GIFT Cloud Improving Usability of Adaptive Tutor Authoring Tools within a Web-based Application

GIFT Cloud is the recently released web-based application version of GIFT, an open-source computer-based tutoring architecture that supports authoring, deployment, and evaluation of intelligent tutoring system technologies. This paper presents the GIFT Cloud Authoring Tools, through the lens of usability. Each major element within the authoring tools is described, along with usability design considerations that were made in order to reduce occurrence of error, to organize information, and to support end-user goals. The initial release of GIFT Cloud supports an iterative design approach, informed by user data and feedback, with an overall goal of making tutor authoring practical for subject matter experts without computer programming or instructional design knowledge. As such, lessons learned from this release, as well as plans for future research and usability improvements, are discussed.