| | | | | | Project Sponsor: Office of Naval Research
 Project Sponsor: Office of Naval Research
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| | | | | | | | | The Adaptive Intelligent Training Environment (AITE) |  |
{AITE}
The Adaptive Intelligent Training Environment (AITE; N00014-07-1-0098) program is a 3-year contract sponsored by the Office of Naval Research. AITE focuses on the application and extension of Augmented Cognition research for military learning environments. This initiative’s goal is to develop a cohesive program of research that supports the implementation of adaptive/individualized selection and training solutions to produce more efficient and effective combat behaviors across the span of Marine Corps missions. To achieve this, the AITE team is integrating cognitive neuroscience and learning science research and development efforts in order to diagnosis a learner’s cognitive state in real time, and then appropriately adapt computer-based learning in response. A final demonstration of capabilities is planned for July 2009; for this demonstration AITE products will be integrated with the USMC’s Deployable Virtual Training Environment (DVTE), a multi-user laptop-based simulation suite fielded world-wide by the Marine Corps.
AITE has two main focus areas:
Operational NeuroscienceA significant portion of the AITE effort addresses science and technology issues related to operational neuroscience, specifically the development and validation of novel analysis and visualization techniques to support real-time “data fusion” from multiple concurrent neurophysiological sensors. The AITE team hypothesizes that increased diagnosticity, sensitivity, and therefore more predictive ability can be achieved through this approach. Particular data inputs applied towards AITE experimental derive from Wearable Arousal Meters (WAM), Electroencephalography (EEG) caps, Respiration sensors, Electrodermal Response (EDR) sensors, InterBeat Intervals (IBI) monitors, and Functional Near-InfraRed imaging (fNIR) devices. These sensors inform the analyses that produce real-time diagnoses of learners’ states. Treatments of suboptimal states are informed by learning science research effort (discussed below).

Learning ScienceA significant portion of the AITE effort also addresses science and technology issues related to the development and experimental validation of targeted strategies for efficiently increasing learning effectiveness. The AITE team posits that current theoretical learning approaches focus on reactions to less precise data (i.e., not to the level of the cognitive state) and that many learning strategies concentrate solely on efficiency (e.g., Cognitive Load Theory) or deeper retention (e.g., Constructivism) while failing to integrate these two goals. Thus, the AITE learning science thrust is focused on developing methodologies that inform precise, real-time responses to learners’ suboptimal cognitive states in support of effective, efficient learning. The diagnostic inputs that inform these treatments are provided by the operational neuroscience research effort (discussed above).
{AITE}
The Adaptive Intelligent Training Environment (AITE; N00014-07-1-0098) program is a 3-year contract sponsored by the Office of Naval Research. AITE focuses on the application and extension of Augmented Cognition research for military learning environments. This initiative’s goal is to develop a cohesive program of research that supports the implementation of adaptive/individualized selection and training solutions to produce more efficient and effective combat behaviors across the span of Marine Corps missions. To achieve this, the AITE team is integrating cognitive neuroscience and learning science research and development efforts in order to diagnosis a learner’s cognitive state in real time, and then appropriately adapt computer-based learning in response. A final demonstration of capabilities is planned for July 2009; for this demonstration AITE products will be integrated with the USMC’s Deployable Virtual Training Environment (DVTE), a multi-user laptop-based simulation suite fielded world-wide by the Marine Corps.
AITE has two main focus areas:
Operational NeuroscienceA significant portion of the AITE effort addresses science and technology issues related to operational neuroscience, specifically the development and validation of novel analysis and visualization techniques to support real-time “data fusion” from multiple concurrent neurophysiological sensors. The AITE team hypothesizes that increased diagnosticity, sensitivity, and therefore more predictive ability can be achieved through this approach. Particular data inputs applied towards AITE experimental derive from Wearable Arousal Meters (WAM), Electroencephalography (EEG) caps, Respiration sensors, Electrodermal Response (EDR) sensors, InterBeat Intervals (IBI) monitors, and Functional Near-InfraRed imaging (fNIR) devices. These sensors inform the analyses that produce real-time diagnoses of learners’ states. Treatments of suboptimal states are informed by learning science research effort (discussed below).

Learning ScienceA significant portion of the AITE effort also addresses science and technology issues related to the development and experimental validation of targeted strategies for efficiently increasing learning effectiveness. The AITE team posits that current theoretical learning approaches focus on reactions to less precise data (i.e., not to the level of the cognitive state) and that many learning strategies concentrate solely on efficiency (e.g., Cognitive Load Theory) or deeper retention (e.g., Constructivism) while failing to integrate these two goals. Thus, the AITE learning science thrust is focused on developing methodologies that inform precise, real-time responses to learners’ suboptimal cognitive states in support of effective, efficient learning. The diagnostic inputs that inform these treatments are provided by the operational neuroscience research effort (discussed above). | |
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Jennifer J. Vogel-Walcutt, Ph.D.
Research Associate, ACTIVE Lab
Team Lead, Learning Initiatives, ACTIVE Lab
Ph.D., Developmental Psychology, Florida State University
M.S., Clinical Psychology, University of Central Florida
B.S., Psychology, Colgate University
Dr. Jennifer Vogel-Walcutt is on the research faculty at UCF’s Institute for Simulation and Training. She graduated from Florida State University with her Ph.D. in developmental psychology and leads the Learning Initiatives team within the ACTIVE Lab. Dr. Vogel-Walcutt’s research focuses on learning efficiency in complex environments. She is currently applying this research to military domains by creating strategies and methodologies for more efficient training.
Dr. Vogel-Walcutt's Curriculum Vitae
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Research Associate
Office: Partnership II- 131 J
Phone: 407-882-1366
jvogel@ist.ucf.edu
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Denise Nicholson, Ph.D., CMSP
Director, ACTIVE Lab;
Senior Research Associate, Institute for Simulation and Training;
Faculty, Modeling and Simulation Graduate Program;
Research Scientist, College of Optics and Photonics/CREOL
M.S., Ph.D., Optical Sciences, University of Arizona
B.S., Electrical Computer Engineering, Clarkson University
Certified Modeling and Simulation Professional (CMSP)
Dr. Nicholson's research focus on human systems modeling, simulation and training includes virtual reality, human–agent collaboration, and adaptive human systems technologies for Department of Defense applications. She joined the university in 2005 with over 18 years of government service ranging from bench-level research at the Air Force Research Lab to leadership as the Deputy Director for Science and Technology at the U.S. Navy's NAVAIR Training Systems Division.
Dr. Nicholson's Curriculum Vitae
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ACTIVE Lab
Director
Office: Partnership II- 319
Phone: 407-882-1444
dnichols@ist.ucf.edu
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Cali Fidopiastis, Ph.D.
Research Associate, ACTIVE Lab
Associate Director, Applied Cognition, ACTIVE Lab
Ph.D., Modeling and Simulation-Human Factors, University of Central Florida
M.A., Experimental Psychology (Sociology), University of California, Irvine
B.S., Biology, University of California, Irvine
B.A., Psychology, University of California, Irvine
Cali is the Associate Director for Applied Cognition for the ACTIVE Lab. As the lead of the Operational Neuroscience Sensing Suite (ONSS) Lab, her team tests and validates the use of psychophysiological measures within simulated training and rehabilitation environments. The essence of her work is to study psychophysiolgical changes of individuals as they perform tasks within naturalistic environments by employing biosensors such as near infrared imaging (NIR) and EEG. These metrics lend to a better understanding of brain and state changes through biomathematical modeling and cognitive neuroscience approaches. This effort also includes developing novel data fusion and visualization techniques for feedback and assessment. The overall goal of her work is to combine the understanding of head-mounted displays, computer graphics, and user perception to create optimal virtual environment training solutions for military, surgical, and rehabilitation applications.
Dr. Fidopiastis' Curriculum Vitae
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Research Associate
Office: Partnership II- 324
Phone: 407-882-1451
cfidopia@ist.ucf.edu
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Cali Fidopiastis, Ph.D.
Research Associate, ACTIVE Lab
Associate Director, Applied Cognition, ACTIVE Lab
Ph.D., Modeling and Simulation-Human Factors, University of Central Florida
M.A., Experimental Psychology (Sociology), University of California, Irvine
B.S., Biology, University of California, Irvine
B.A., Psychology, University of California, Irvine
Cali is the Associate Director for Applied Cognition for the ACTIVE Lab. As the lead of the Operational Neuroscience Sensing Suite (ONSS) Lab, her team tests and validates the use of psychophysiological measures within simulated training and rehabilitation environments. The essence of her work is to study psychophysiolgical changes of individuals as they perform tasks within naturalistic environments by employing biosensors such as near infrared imaging (NIR) and EEG. These metrics lend to a better understanding of brain and state changes through biomathematical modeling and cognitive neuroscience approaches. This effort also includes developing novel data fusion and visualization techniques for feedback and assessment. The overall goal of her work is to combine the understanding of head-mounted displays, computer graphics, and user perception to create optimal virtual environment training solutions for military, surgical, and rehabilitation applications.
Dr. Fidopiastis' Curriculum Vitae
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Research Associate
Office: Partnership II- 324
Phone: 407-882-1451
cfidopia@ist.ucf.edu
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Larry Davis, Ph.D.
Research Associate, Interfaces and Applications, ACTIVE Lab
M.S., Ph.D., Electrical Engineering, University of Central Florida
B.S., Electrical Engineering, Florida A&M University
Larry Davis has been a member of the ACTIVE Laboratory since 2006. Davis’ research interests include simulation architectures and system development, virtual environment system design and implementation, user interfaces for computing environments, game-based simulation and training, tracking in virtual environments, augmented cognition, and 3D visualization.
Dr. Davis' Curriculum Vitae
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Research Associate
Office: Partnership II- 328
Phone: 407-882-2218
ldavis@ist.ucf.edu
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Juliana Beatriz Gebrim
Graduate Research Assistant, ACTIVE Lab
M.S., Modeling and Simulation, University of Central Florida (In Progress)
B.S., Mechanical Engineering, University of Sao Paulo
Juliana is a Graduate Research Assistant at the Institute for Simulation and Training. She graduated as a Mechanical Engineer from the University of Sao Paulo. Now she seeks a Master's degree in Modeling and Simulation at UCF and works with the Learning Initiatives team within the ACTIVE Lab. Her current main focus is to study how to improve military training efficiency. |
Graduate Research Assistant
jgebrim@ist.ucf.edu
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Logan Fiorella
Undergrad Research Associate, ACTIVE Lab
B.S., Psychology, University of Central Florida (In Progress)
Logan is part of the Learning Initiatives team at the ACTIVE Lab. He joined the lab in Spring of 2008. He is presently completing his Bachelor's in General Psychology, after which he plans pursuing graduate study in Human Factors.
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Undergraduate Research Assistant
Office: Partnership II- 107
lfiorell@ist.ucf.edu
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Julian Abich IV
Graduate Research Assistant, ACTIVE Lab
M.S., Modeling and Simulation, University of Central Florida (In Progress)
B.S., Psychology, University of Central Florida
Julian became a member of the ACTIVE lab in 2009 after working for the Minds in Technology/Machines in Thought (MIT2) lab for almost three years. Within the MIT2 lab, he was a part of the Scalable Research Design group which focused on the understanding of visual display design and operation within a variety of settings. Julian is currently working with the Learning Initiatives team within ACTIVE to gain a better grasp of user perspective in hopes of incorportating this information with human system dynamics.
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Graduate Research Assistant
Office: Partnership II 230 or Psychology 209
Phone: 407-823-0918
jabich@ist.ucf.edu |
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Heather Flynn
Undergraduate Research Assistant, ACTIVE Lab
B.S., Environmental Science, University of Central Florida (In Progress)
Heather joined the Learning Initiatives team within the ACTIVE Lab in the spring of 2009. She is presently completing her Bachelor's degree in Environmental Science, after which she plans to pursue graduate study in Ecology and Evolutionary Biology. |
Undergraduate Research Assistant
hflynn@ist.ucf.edu
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Julie Drexler, Ph.D.
Reasearch Associate, ACTIVE Lab
Associate Director, Adaptive Automation, ACTIVE Lab
Ph.D., Industrial Engineering, University of Central Florida
M.S., Human Engineering/Ergonomics, University of Central Florida
B.A., Psychology, University of Central Florida
Prior to joining the ACTIVE lab, Dr. Drexler was a Scientist at a human factors psychology research and development (R&D) firm where she worked for more than 11 years developing and managing multivariate theoretical and applied human factors R&D projects sponsored by the Department of Defense and other government agencies (DARPA, NASA, NSF, and NIH). Her human factors research experience includes human-computer interaction in flight simulators and virtual reality devices; quantifying the effects of practice on information processing; human performance measurement; individual differences in visual perception and postural equilibrium; and development of computerized tests of human capability (i.e., perception, cognition, etc.). She also has experience in the identification and evaluation of human factors issues during the operational test and evaluation (T&E) of military systems.
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Research Associate
Office: Partnership II- 334
Phone: 407-882-2115
jdrexler@ist.ucf.edu
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Daniel Barber
Research Associate, ACTIVE Lab
Team Lead, Robotics and Intelligent Systems, ACTIVE Lab
Ph.D., Modeling and Simulation, University of Central Florida (In Progress)
B.S., M.S., Computer Engineering, University of Central Florida
Daniel Barber has extensive experience in the field of robotics, with research in intelligent systems, machine learning, human-agent collaboration, control systems, path-planning, computer vision, communication frameworks, and environment modeling. He has designed multiple autonomous systems and is a faculty advisor and mentor for the Robotics Club at the University of Central Florida. Daniel is also a member of the Joint Architecture for Unmanned Systems (JAUS) Working Group; which facilitates interoperability within unmanned systems. His current research focus is Live, Virtual, and Constructive simulations involving mixed-initiative teams and hybrid machine learning models for human, social, cultural, and behavior modeling.
Mr. Barber's Curriculum Vitae
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Research Associate
Office: Partnership II- 335
Phone: 407-882-1128
dbarber@ist.ucf.edu
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Sergey Leontyev
Research Assistant, ACTIVE Lab
Programmer, Technology Development Lab
B.S. Computer Science, University of Central Florida
Sergey Leontyev is a programmer in the ACTIVE Laboratory at the Institute for Simulation and Training. He holds a B.S. degree in Computer Science, with a background in 3D graphics, computer vision, and algorithms in general. His current interests include self-calibration, multiple projector systems, 2-pass rendering, and other challenging topics.
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Research Assistant
Office: Partnership II- Cubicle 335 / 112
Phone: 407-882-0291
sleontye@ist.ucf.edu
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Bo Sun
Graduate Research Assistant, ACTIVE Lab
M.S., Computer Science, University of Central Florida (In Progress)
B.S., Computer Science, University of Central Florida
Mr. Sun’s primary research delves into user-oriented performance measurement architectures that facilitate feedback and mitigation. He also utilizes his programming skills to help advance Learning Initiatives at the ACTIVE Lab.
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Graduate Research Assistant
Office: Partnership II- Cube 325-B
Phone: 407-882-1498
bsun@ist.ucf.edu
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Michelle Fox
Graduate Research Assistant, ACTIVE Lab
M.S., Computer Engineering, University of Central Florida (In Progress)
B.A., Psychology, University of Wisconsin - Milwaukee
Michelle Fox is a Graduate Research Assistant for the ACTIVE lab. As a member of the Operational Neuroscience Sensing Team, she assists with the analysis of psychophysiological data. She is currently completing her Masters in Computer Engineering at the University of Central Florida. Her research interests include machine learning, intelligent systems, human perception and cognition.
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Graduate Research Assistant
Office: Partnership II- 339-B
Phone: 407-882-2112
mfox@ist.ucf.edu
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Sae Schatz, Ph.D.
Research Associate, ACTIVE Lab
Ph.D., Modeling and Simulation, University of Central Florida
M.S., Modeling and Simulation, University of Central Florida
B.S., Computer Information Technology, University of Central Florida
Dr. Schatz is a Research Associate with the ACTIVE laboratory at the University of Central Florida’s Institute for Simulation and Training. The ACTIVE lab, directed by Dr. Denise Nicholson, engages in applied research and development for the analysis and improvement of human performance. Among its many other activities, the lab develops validated simulation-based training systems for Defense Agencies, including the United States Army, Navy, and Marine Corps. Dr. Schatz’s work comprises applied research efforts in simulation environments, including the investigation of intelligent and adaptive training systems and the development of training architectures in support of the lab’s military training efforts.
Dr. Schatz's Curriculum Vitae
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Research Associate
Office: Partnership II- 335
Phone: 407-882-1483
sschatz@ist.ucf.edu
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J. T. Folsom-Kovarik
Graduate Research Assistant, ACTIVE Lab
M.S., Computer Science, University of Central Florida
B.A., Computer Science and Philosophy, Drew University
J. T. joined the ACTIVE Lab in 2009 to work on automated assessment, adaptation, and other machine learning capabilities in the NEW-IT project.
He is a PhD candidate in the Computer Science department at UCF, where his research helps develop the NEAT, HyperNEAT, and related methods for evolving neural networks.
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Graduate Research Assistant
Office: Partnership II- 335-E
jfolsomk@ist.ucf.edu
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Stephanie Lackey, Ph.D.
Deputy Director, ACTIVE Lab;
Senior Research Associate, Institute for Simulation and Training;
Faculty, Modeling and Simulation Graduate Program
M.S., Ph.D, Industrial Engineering and Management Systems, University of Central Florida
B.S., Mathematics, Methodist University
Dr. Lackey researches methods to transition Science and Technology (S&T) prototypes to downstream acquisition functions and live training environments. This work is influenced by established as well as emerging trends in systems engineering, project management, and industrial engineering. Dr. Lackey became a member of the ACTIVE Lab in 2008 following seven years of Government service with the U.S. Navy’s Naval Air Warfare Center Training Systems Division (NAVAIR TSD). Her efforts at NAVAIR focused on high risk research and development aimed at rapid transition of virtual communications capabilities to the field and Fleet.
Dr. Lackey's Curriculum Vitae
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ACTIVE Lab
Deputy Director
Office: Partnership II- 332
Phone: 407-882-2427
slackey@ist.ucf.edu
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Stephanie Lackey, Ph.D.
Deputy Director, ACTIVE Lab;
Senior Research Associate, Institute for Simulation and Training;
Faculty, Modeling and Simulation Graduate Program
M.S., Ph.D, Industrial Engineering and Management Systems, University of Central Florida
B.S., Mathematics, Methodist University
Dr. Lackey researches methods to transition Science and Technology (S&T) prototypes to downstream acquisition functions and live training environments. This work is influenced by established as well as emerging trends in systems engineering, project management, and industrial engineering. Dr. Lackey became a member of the ACTIVE Lab in 2008 following seven years of Government service with the U.S. Navy’s Naval Air Warfare Center Training Systems Division (NAVAIR TSD). Her efforts at NAVAIR focused on high risk research and development aimed at rapid transition of virtual communications capabilities to the field and Fleet.
Dr. Lackey's Curriculum Vitae
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ACTIVE Lab
Deputy Director
Office: Partnership II- 332
Phone: 407-882-2427
slackey@ist.ucf.edu
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Benjamin S. Goldberg
Graduate Research Assistant, ACTIVE Lab
Institute for Simulation and Training at UCF
M.S., Modeling and Simulation, University of Central Florida (In Progress)
B.A., Sociology; Minor in Business Administration, University of Florida
Mr. Goldberg’s role in the ACTIVE Lab is to provide support and research to various projects. The focus of his research is concentrated on two main topic areas: Systems Engineering and Human Systems Integration. Mr. Goldberg’s primary tasks include interfacing with external entities in support of directed research tasks, and conducting literature reviews for the purpose of summarizing, prioritizing, and synthesizing interdisciplinary research materials. Mr. Goldberg also assists in the development of the Systems Engineering process for research and development projects within the ACTIVE Lab.
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Graduate Research Assistant
Office: Partnership II- 336
Phone: 407-882-1396
bgoldber@ist.ucf.edu
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Conceptual and architectural design process of an SBT-AID training system.
Schatz, S., Bowers, C. A., & Nicholson, D. (2009). Conceptual and architectural design process of an SBT-AID training system. Poster presented at the 18th Annual Conference on Behavior Representation in Modeling and Simulation (BRIMS), Sundance, UT, March 31-April 2, 2009.
Abstract:
In this poster session, we discuss our approach to designing and building an advanced training system that combines intel-ligent tutoring components, scenario-based instructional simu-lations, dynamic scenario generation capabilities, content au-thoring support, and self-validating machine learning tech-niques. We call the conceptual design Scenario-Based Train-ing: Adaptive, Intelligent, Dynamic (SBT-AID). Defining the critical components of a scenario to support higher-order KSA training.
Schatz, S. & Bowers, C. (2009). Defining the critical components of a scenario to support higher-order KSA training. In Denise Nicholson (Chair) Developing adaptive, intelligent scenarios to support enhanced operations training. Session presented at the 2009 Military Modeling and Simulation Symposium (MMS'09), San Diego, CA, March 22-27, 2009.
Abstract:
Today’s defense agencies recognize that warfighters must be both physically and cognitively prepared. The notion of ‘cognitive readiness’ addresses human optimization (Etter, 2002), and it means that individuals have the mental tools (i.e., skills, knowledge, abilities, motivations, and personal dispositions) that they need to “sustain competent performance in the complex and unpredictable environment of modern military operations” (Morrison & Fletcher, 2002: ES-1). Training higher-order knowledge, skills, and attitudes (KSAs) contributes to cognitive readiness and supports the mission of Enhanced Operations.
Scenario-based training (SBT) is an instructional approach ideally suited to take advantage of the characteristics of simulation, and many have demonstrated the efficacy of SBT to support the training of higher-order skills (e.g., Hays, Jacobs, Prince, & Salas, 1992; Salas, Bowers, & Rhodenizer, 1998; Oser, Gualtieri, Cannon-Bowers, & Salas, 1999; Klein, Salas, Burke, Goodwin, Halpin, Diaz Granados, & Badum, 2005). Many of the characteristics that enable SBT to so effectively support training are already established and documented, albeit somewhat ambiguously (i.e., using human readable terms) (see Stout, 2008 for a review). However, as we move away from human-driven SBT and toward automation of instructor processes, it becomes necessary to be more precise about the scenario features (or combinations of features) that support training efficacy.
Specifically, we need to objectively and measurably define the critical cues within a scenario that create the appropriate circumstances in which to practice and assess higher-order KSAs. Doing so will support advanced adaptive and intelligent simulation-based training technologies by informing heuristics for automatic scenario generation and adaptation software. More generally, this work may also enhance the validity of scenario-based training and assessment of cognitive readiness.
During this panel, we will present an interim version of a taxonomy that outlines the research-derived features believed to contribute to the effectiveness of a scenario. Each listing includes information about the element’s objective measurement criteria (i.e., using machine usable specifications) that are theoretically linked to training performance outcomes. The ultimate goal of this work is to derive discrete rules that can be used to guide dynamic creation and intelligent adaptation of scenarios within a virtual simulation. This panel will describe ongoing research in service of the U.S. Marine Corps.
Estimation of arousal using decomposed skin conductance features
Vartak, A.A., Fidopiastis, C., Nicholson, D., Mikhael, M., "Estimation of arousal using decomposed skin conductance features", to appear in Biomedical Sciences Instrumentation 2009.
Abstract:
Electrodermal response (EDR) shows characteristic signal patterns that correspond to different emotional states. The first major step in using EDR for estimation of emotional state is the separation of various tonic and phasic components. This separation of components is more challenging when the responses overlap each other as they do when responding within shorter inter-stimulus interval. A mathematical model fitting procedure, which separates these overlapping components, is used in an experiment, where participants (n=18) were shown stimuli from the International Affective Picture System (IAPS), which varied by levels of arousal and valance. The EDR signal is collected during the experiment, and features are extracted using the mathematical model fitting procedure. These features are further used, to classify the EDR signal into high versus low arousal responses. A simple k-nearest neighbor algorithm is used to classify the features with 74% accuracy. The accuracy level obtained by a single sensor emphasizes the fact that use of specific feature extraction methods for multi-sensor applications is critical to the classification accuracy. We discuss these results in relation to adaptive system trainer design where multiple biosensors are currently being explored to assess the cognitive state of the learner.
Interactions and Training with Unmanned Systems and the Nintendo Wiimote.
Varcholik, P., Barber, D., Nicholson, D. (2008). “Interactions and Training with Unmanned Systems and the Nintendo Wiimote.” Pending publication in the Proceedings of the Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, 2008.
Abstract:
As unmanned systems continue to evolve and their presence becomes more prolific, new methods are needed for training people to interact with these systems. Likewise, new interfaces must be developed to take advantage of the increasing capabilities of these platforms. However, the complexity of such interfaces must not grow in parallel with advancements in unmanned systems technology. A common form of human communication is through the use of arm and hand gestures. Applying gesture-based communication methods to human-to-robot communication may increase the interface capabilities, resulting in less complex, natural and intuitive interfaces. In the context of military operations, hand and arm gestures (such as those listed in the Army Field Manual on Visual Signals, FM 21-60) may be used to communicate tactical information and instructions to robotic team members. We believe that a gesture-based interface provides a natural method for controlling unmanned systems and reduces training time and training costs for military personnel by reusing standard gestures. The research presented explores these hypotheses through interactions with unmanned systems using computer-mediated gesture recognition. The methodology employs the Nintendo Wii Remote Controller (Wiimote) to retrieve and classify one- and two-handed gestures that are mapped to an unmanned system command set. To ensure interoperability across multiple types of unmanned systems, our technology uses the Joint Architecture for Unmanned Systems (JAUS); an emerging standard that provides a hardware and software independent communication framework. In this paper, a system is presented that uses inexpensive, commercial off-the-shelf (COTS) technology for gesture input to control multiple types of unmanned systems. A detailed discussion of the technology is provided with a focus on operator usability and training. Finally, to explore the efficacy of the interface, a usability study is presented where participants perform a series of tasks to control an unmanned system using arm and hand gestures. The Mixed Initiative Experimental (MIX) Testbed for Human Robot Interactions with Varied Levels of Automation.
Barber, D., Davis, L., Nicholson, D., Chen, J.Y.C., Finkelstein, N. (2008). “The Mixed Initiative Experimental (MIX) Testbed for Human Robot Interactions with Varied Levels of Automation.” Pending publication in the Proceedings of the 26th Army Science Conference, Orlando, 2008.
Abstract:
In 2007, the U.S. Department of Defense (DoD) released a report detailing the future of robotic military equipment and how to proceed in development and procurement of unmanned systems (Office of Secretary of Defense, 2007). This document recognizes the role of unmanned systems in the areas of reconnaissance, surveillance, and target identification. This role for unmanned systems implies a need for improved warfighter support and training. The Mixed Initiative Experimental (MIX) Testbed is a distributed simulation environment which provides a means for this type of training. This paper describes the design and capabilities of the MIX Testbed and exemplar scenarios for research and training with unmanned systems of varying capabilities. Augmented cognition and training in the laboratory: DVTE system validation.
Vogel-Walcutt, J.J., Schatz, S., Bowers, C.A., & Nicholson, D. (2008). Augmented cognition and training in the laboratory: DVTE system validation. Paper to be presented at the 52nd annual meeting of the Human Factors and Ergonomic Society, New York, NY, September 22-26, 2008.
Abstract:
As the modern workplace becomes more complex, the training community needs to develop new strategies to continute to create a competent workforce. The emerging field of augmented cognition may be able to contribute greatly to increasing training capability through the use of neurophysiological measures that support real-time performance and can be used to satisfy some of the requirements of an automated intelligent tutoring system. The first step to building this system is to design and validate a testbed that can be used with future efforts to target effective mitigation strategies with learning efficiency. In this study, participants watched a computerized instructional presentation and then engaged in a practice CFF scenario in the simulator. When finished, each participant was assigned to either a low or high task load test scenario. In both, the goal was to destroy five enemy tanks. Some participants were also asked to simultaneously execute a secondary radar monitoring task. Both the National Aeronautics and Space Administration’s Task Load Index (NASA-TLX) and the MRQ were used to assess the validity of the testbed. Results from both measures indicate that a significant difference exists between the two levels of workload and further, we can distinguish between the subscales within each measurement tool. Thus, the overarching goal of this study was achieved. High and low workload scenarios were created and validated. Ultimatly, they will be used as a testbed of scenarios to address the questions surrounding the use of neurophysiological equipment to impact individual learning patterns. Trait arousability: An individual difference that may actually be useful for designers.
Schatz, S.L., Bowers, C.A., Nicholson, D., & Vogel-Walcutt, J.J. (2008). Trait arousability: An individual difference that may actually be useful for designers. Abstract accepted for the 2nd International Conference on Applied Ergonomics, Las Vegas, NV, July 14-17.
Abstract:
In this paper we describe trait arousability, an individual variation in how people react to high-information stimuli. Arousable individuals are particularly sensitive to stimuli, and consequently, experience high arousal states more often and for longer durations. Since arousal impacts many cognitive processes (e.g., attention, anxiety, memory, stress, and motivation), variations in arousability meaningfully impact many downstream events (including overall performance). Consequently, we encourage designers to consider this individual difference, and we offer preliminary suggestions on how to best measure, and design for, this phenomenon. The Mixed-Initiative Experimental Testbed for Collaborative Human Robot Interactions.
Barber, D.J., Leontyev, S., Sun, B., Davis, L., Chen, J.Y.C., Nicholson, D., (2008). “The Mixed-Initiative Experimental Testbed for Collaborative Human Robot Interactions.” In the Proceedings of the 2008 International Symposium on Collaborative Technologies and Systems, IEEE, 2008.
Abstract:
Current military forces increasingly rely upon unmanned systems. Training mixed teams of soldiers and robotic agents can be accomplished using specialized virtual environments or fabricated real-life structures. However, there are still gaps in the technology and methodologies needed to support human-robot teams. In addition, the environments presently used aren’t reconfigurable or extendable. The Mixed-Initiative Experimental (MIX) Testbed was developed to support training of mixedinitiative teams, experimentation with new training methods, and exploration of team composition and robot capabilities. The testbed combines the use of the Joint Architecture for Unmanned Systems (JAUS) and High Level Architecture (HLA) to create a system that can be used with a combination of virtual robotic entities within an HLA-based simulation environment. In this paper we will present the design and implementation of the MIX Testbed and describe sample scenarios for experimentation and training. New Kids on the Block: Multi-dimensional perspectives on Augmented Cognition
Drexler, J. M., & Reeves, L. M. (2008). New Kids on the Block: Multi-dimensional perspectives on Augmented Cognition. Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting (pp. 154-156). Santa Monica, CA: Human Factors and Ergonomics Society.
Abstract:
This discussion panel was organized to offer HFES members an opportunity to learn more about the burgeoning field of Augmented Cognition and to discover the multi-dimensional aspects of the discipline. The session will feature six invited panelists who were selected to represent a cross-section of the Augmented Cognition International Society community of more than 900 members. Each panelist will present their unique perspective of the AugCog field, which will provide the audience with information on a variety of research, development, and application areas in the AugCog field within the U.S and abroad. The panel members and their associated AugCog perspectives include: CDR Dylan Schmorrow, government; Denise Nicholson, academia; Dennis McBride, non-profit; Kay Stanney and Chris Berka, industry; and Blair Dickson, industry/international. Assessing virtual rehabilitation design with biophysiological metrics.
Fidopiastis, C. M., Hughes, C. E., Smith, E. M. & Nicholson, D. M. (2007). Assessing virtual rehabilitation design with biophysiological metrics. Proceedings of Virtual Rehabilitation 2007, Venice, Italy.
Abstract:
Efficacy of virtual rehabilitation applications is typically demonstrated by pre and post comparisons of observable behavioral metrics. These behaviors can be monitored via devices such as trackers or video capture and more traditional error rate metrics. However, monitoring the patient¿s emotional and cognitive changes during virtual rehabilitation may better guide the rehab process as well as the design of the rehab scenario. We explored the use of biophysiological metrics (EEG, GSR, and Respiration) in the design of a virtual restaurant for the purpose of engaging persons who stutter in verbal interactions during an everyday experience. The EEG results showed that participants experienced higher engagement in the virtual restaurant. Although respiration and GSR metrics differed for each participant, they correlated well with stressors presented in the scenario. The work supports the use of biophysiological measures as an objective means of assessing virtual rehabilitation protocols. Aiding tomorrow’s Augmented Cognition researchers through modeling and simulation curricula.
Drexler, J., Shumaker, R., Nicholson, D., & Fidopiastis, C. (2007). Aiding tomorrow’s Augmented Cognition researchers through modeling and simulation curricula. In D. D. Schmorrow & L. M. Reeves (Eds.), Foundations of Augmented Cognition, HCII 2007 (LNAI 4565; pp. 415-423). Berlin: Springer-Verlag.
Abstract:
Research in the newly emerged field of Augmented Cognition (AugCog) has demonstrated great potential to develop more intelligent computational systems capable of monitoring and adapting the systems to the changing cognitive state of human operators in order to minimize cognitive bottlenecks and improve task performance. As the AugCog field rapidly expands, an increasing number of researchers will be needed to conduct basic and applied research in this burgeoning field. However, due to its multidisciplinary nature and cutting-edge technological applications, most traditional academic disciplines cannot support the training needs of future AugCog researchers. Accordingly, an established Modeling and Simulation (M&S) graduate curriculum is described, which provides a broad basis of interdisciplinary knowledge and skills as well as depth of knowledge within a specific area of the M&S field. Support for use of the flexible M&S curriculum to provide the requisite multifaceted foundational training in Augmented Cognition principles is also presented. Aiding tomorrow’s Augmented Cognition researchers through modeling and simulation curricula.
Drexler, J., Shumaker, R., Nicholson, D., & Fidopiastis, C. (2007). Aiding Tomorrow’s Augmented Cognition Researchers through Modeling and Simulation Curricula. Proceedings of HCI International 2007, Beijing, China.
Abstract:
Research in the newly emerged field of Augmented Cognition (AugCog) has demonstrated great potential to develop more intelligent computational systems capable of monitoring and adapting the systems to the changing cognitive state of human operators in order to minimize cognitive bottlenecks and improve task performance. As the AugCog field rapidly expands, an increasing number of researchers will be needed to conduct basic and applied research in this burgeoning field. However, due to its multidisciplinary nature and cutting-edge technological applications, most traditional academic disciplines cannot support the training needs of future AugCog researchers. Accordingly, an established Modeling and Simulation (M&S) graduate curriculum is described, which provides a broad basis of interdisciplinary knowledge and skills as well as depth of knowledge within a specific area of the M&S field. Support for use of the flexible M&S curriculum to provide the requisite multifaceted foundational training in Augmented Cognition principles is also presented. The role of individual differences in virtual environment-based training
Bowers, C., Vogel-Walcutt, J.J., Cannon-Bowers, J. (2008). The role of individual differences in virtual environment-based training. In D. Schmorrow, J. Cohn, & D. M. Nicholson (Eds.), The PSI Handbook of Virtual Environments for Training and Education: Vol.1. Learning, Requirements, and Metrics, pp.31-50. Westport, CN: Praeger Security International.
Introduction:
Most modern theories of learning and instructional design converge on the conclusion that the attributes that learners bring to the instructional environment are important ingredients in the learning process. In fact, the notion that learners bring a unique set of knowledge, skills, aptitudes, abilities, preferences and experiences to a learning environment is captured by a popular approach known as learner-centered instruction (e.g., see CGTV, 2000; Bransford, Brown & Cocking, 1999; Kanfer & McCombs, 2000; Clark & Wittrock, 2000). Essentially, proponents of this approach argue that characteristics of the learner must be taken into account in the design and delivery of instruction, and that an explicit attempt must be made to build on the strengths of the student. Variables that have been implicated in this regard include: prior knowledge, prior skill, prior experience, misconceptions, and interests, among others. In addition, a number of other personal attributes have been shown to affect learning. These include: motivation (CGTV, 2000 Clark; in press, Clark & Wittrock, 2000, Bransford, Brown & Cocking, 1999), personal agency/self-efficacy (Clark, in press; Kanfer & Combs, 2000; Bandura, 1977; 1986; Gist, 1992); goal orientation (Dweck, 1986; Dweck & Legget, 1988; Bransford, Brown & Cocking, 1999; Kanfer & McCombs, 2000); goal commitment (Clark, in press; Kanfer & McCombs, 2000); emotional state (Clark, in press); self-regulation (Kanfer & McCombs, 2000); misconceptions (Gentner, 1983); interest (Kanfer & McCombs, 2000); instrumentality (Tannenbaum, Matheiu, Salas & Cannon-Bowers, 1991); ability (Mayer, 2001); and spatial ability (Mayer, 2001).
Using virtual reality with and without gaming attributes for academic achievement.
Vogel, J., Greenwood-Ericksen, A., Cannon-Bowers, J., & Bowers, C.B., (2006). Using virtual reality with and without gaming attributes for academic achievement. Journal of Research on Technology in Educational.
Abstract:
A subcategory of computer-assisted instruction (CAI), games have additional attributes such as motivation, reward, interactivity, score, and challenge. This study used a quasi-experimental design to determine if previous findings generalize to non simulation-based game designs. Researchers observed significant improvement in the overall population for math skills in the non-game CAI control condition, but not in the game-based experimental condition. The study found no meaningful, significant differences in language arts skills in any of the conditions. This finding has implications for the design of future learning games, suggesting that a simulation-based approach should be integrated into the gaming technology. (Contains 4 tables.)
Computer Gaming and Interactive Simulations for Learning: A Meta-Analysis
Vogel, J., Vogel, D., Cannon-Bowers, J., Bowers, C.B., Muse, K., & Wright, K. (2006). Computer and interactive simulations for learning: A meta-analysis. Journal of Educational Computing Research, 34(3), 229-243.
Abstract:
Substantial disagreement exists in the literature regarding which educational technology results in the highest cognitive gain for learners. In an attempt to resolve this dispute, we conducted a meta-analysis to decipher which teaching method, games and interactive simulations or traditional, truly dominates and under what circumstances. It was found that across people and situations, games and interactive simulations are more dominant for cognitive gain outcomes. However, consideration of specific moderator variables yielded a more complex picture. For example, males showed no preference while females showed a preference for the game and interactive simulation programs. Also, when students navigated through the programs themselves, there was a significant preference for games and interactive simulations. However, when teachers controlled the programs, no significant advantage was found. Further, when the computer dictated the sequence of the program, results favored those in the traditional teaching method over the games and interactive simulations. These findings are discussed in terms of their implications for exiting theoretical positions as well as future empirical research. (Contains 1 table.) Collaborative Human Robot Interactions in Combined Arms Operations
Barber, D.J., Davis, L., Kemper, D., Smith, P., Nicholson, D. (2007). Collaborative human robot interactions in combined arms operations. Proceedings of the 2007 International Symposium on Collaborative Technologies and Systems. Orlando, FL.
Abstract:
Our military's current and future forces are operating in a highly distributed, network centric environment which relies heavily on multidisciplinary, mixed-initiative, distributed heterogeneous teams to accomplish missions. This capability requires a high degree of situational awareness and coordination between team members. For combined arms operations, these teams include dismounted and vehicle based soldiers whose platforms can be manned or unmanned (robotic). The robotic entities range from remotely operated to fully autonomous agents. Although there has been much research on human team performance, there still remain many questions for optimizing operational systems with performance support for these ldquomixed-initiativerdquo teams. For instance, the design of todaypsilas human-robot team interfaces focuses on the skills required to operate an unmanned system. However, such designs do not address methods for operating within a distributed, mixed-initiative team. There are critical gaps in understanding the technology and methodologies needed to support the human-robot team as part of a larger, heterogeneous team. This paper will discuss research targeted at these gaps. We present the design of a distributed simulation, a virtual environment, and a human-robot interaction (HRI) team test bed. The HRI test bed includes reconfigurable multimodal interfaces to explore collaborative HRI challenges within combined arms missions. Exemplar scenarios and experimental designs for future planned studies will also be presented. Operator workload and heart-rate variability during a simulated reconnaissance mission with an unmanned ground vehicle.
Chen, J. Y. C., Drexler, J. M., Sciarini, L. W., Cosenzo, K. A., Barnes, M. J., & Nicholson, D. (in press). Operator workload and heart-rate variability during a simulated reconnaissance mission with an unmanned ground vehicle. Proceedings of the 2008 Army Science Conference.
Abstract:
In this study, we simulated a generic mounted crewstation environment and conducted an experiment to examine the workload and performance of the operator of a ground robot. Participants were randomly assigned to four tasking conditions: robotics tasks only, robotics plus an auditory task, robotics plus a visual monitoring task, or all three tasks simultaneously. Participants completed four mission scenarios. In two of these scenarios, their robot was semi-autonomous. In the other two scenarios, they had to teleoperate the robot. An Aided Target Recognition (AiTR) system was available to help them with their target detection tasks in only two of the four scenarios. Results showed that operators’ situational awareness and perceived workload were significantly worse when they teleoperated the robot. Individual differences factors such as the operator’s spatial ability and attentional control were also investigated. Implications for military personnel selection were discussed.
Collaborative Human Robot Interactions in Combined Arms Operations
Barber, D.J., Davis, L., Kemper, D., Smith, P., Nicholson, D., "Collaborative Human Robot Interactions in Combined Arms Operations", Proceedings in the 2007 International Symposium on Collaborative Technologies and Systems, IEEE.
Abstract:
Our military's current and future forces are operating in a highly distributed, network centric environment which relies heavily on multidisciplinary, mixed-initiative, distributed heterogeneous teams to accomplish missions. This capability requires a high degree of situational awareness and coordination between team members. For combined arms operations, these teams include dismounted and vehicle based soldiers whose platforms can be manned or unmanned (robotic). The robotic entities range from remotely operated to fully autonomous agents. Although there has been much research on human team performance, there still remain many questions for optimizing operational systems with performance support for these ldquomixed-initiativerdquo teams. For instance, the design of todaypsilas human-robot team interfaces focuses on the skills required to operate an unmanned system. However, such designs do not address methods for operating within a distributed, mixed-initiative team. There are critical gaps in understanding the technology and methodologies needed to support the human-robot team as part of a larger, heterogeneous team. This paper will discuss research targeted at these gaps. We present the design of a distributed simulation, a virtual environment, and a human-robot interaction (HRI) team test bed. The HRI test bed includes reconfigurable multimodal interfaces to explore collaborative HRI challenges within combined arms missions. Exemplar scenarios and experimental designs for future planned studies will also be presented.
Developing baseline assessments for virtual rehabilitation environments
Fidopiastis, C. M., Hughes, C. E., Smith, E. M., Nicholson, D. M., "Developing baseline assessments for virtual rehabilitation environments", Proceedings of the 4th International INTUITION Conference.
Abstract:
Current human-computer interaction,guidelines for the design of virtual rehabilitation environments are usability centered and do not include benchmarks of the technology within a user-in-the-loop design cycle. Without including user performance benchmarks prior to implementing virtual environment based rehabilitation protocols, separating true performance outcomes from those imposed by the technology may be difficult. More seriously, the conclusions drawn from such rehabilitation protocols may be inaccurate. Given new unobtrusive, wireless, and wearable psychophysical sensing devices such as EEG and fNIR, therapists can measure important constructs such as task engagement and workload to develop norms with which to compare against patient performance. Coupled with traditional task analyses and behavioral metrics such as time on task and error rates, new baseline assessments can guide the virtual environment design cycle and may provide more meaningful outcome measures of cognitive recovery. Keywords: Virtual rehabilitation, User-centered design, Augmented and mixed reality. Applied cognition and training research to address emerging military requirements
Nicholson, D., Davis, L., Fidopiastis, C., "Applied cognition and training research to address emerging military requirements", Proceedings of SPIE Defense & Security Symposium. (6564-15, Session 4).
Abstract:
Modeling, Simulation and Training (MS&T) technologies have provided significant capabilities for Military training and mission rehearsal. However, most of the state-of-the-art MS&T systems used today are high fidelity, stand alone systems, routinely staffed by a team of support and instructional personnel. As the military becomes more reliant on these technologies to support ever changing concepts of operations, they are asking for numerous technological advancements including 1) automated instructional features to reduce the number of personnel required for exercises, 2) increased capability for adaptation of human computer interfaces to support individual differences and embedded performance support in operational settings, and 3) a continuum of low to high fidelity system components to provide embedded, deployable and transportable solutions. A multi-disciplinary team of researchers at the University of Central Florida's (UCF) Institute for Simulation and Training (IST) Applied Cognition and Training in Immersive Virtual Environments Lab (ACTIVE), lead by Dr. Denise Nicholson, is performing research and development to address these emerging requirements as part of on-going projects for Navy, Marine Corps and Army customers. In this paper we will discuss some of the challenges that confront researchers in this area and how the ACTIVE lab hopes to respond to these challenges.
Collaborative human robot interactions in combined arms operations
Barber, D.J., Davis, L., Kemper, D., Smith, P., Nicholson, D., "Collaborative human robot interactions in combined arms operations", Paper Presented at the 2007 International Symposium on Collaborative Technologies and Systems.
Abstract:
Our military's current and future forces are operating in a highly distributed, network centric environment which relies heavily on multidisciplinary, mixed-initiative, distributed heterogeneous teams to accomplish missions. This capability requires a high degree of situational awareness and coordination between team members. For combined arms operations, these teams include dismounted and vehicle based soldiers whose platforms can be manned or unmanned (robotic). The robotic entities range from remotely operated to fully autonomous agents. Although there has been much research on human team performance, there still remain many questions for optimizing operational systems with performance support for these ldquomixed-initiativerdquo teams. For instance, the design of todaypsilas human-robot team interfaces focuses on the skills required to operate an unmanned system. However, such designs do not address methods for operating within a distributed, mixed-initiative team. There are critical gaps in understanding the technology and methodologies needed to support the human-robot team as part of a larger, heterogeneous team. This paper will discuss research targeted at these gaps. We present the design of a distributed simulation, a virtual environment, and a human-robot interaction (HRI) team test bed. The HRI test bed includes reconfigurable multimodal interfaces to explore collaborative HRI challenges within combined arms missions. Exemplar scenarios and experimental designs for future planned studies will also be presented. An adaptive instructional architecture for training and education
Nicholson, D., Fidopiastis, C., Davis, L., Schmorrow, D., & Stanney, K., "An adaptive instructional architecture for training and education", Proceedings of 3rd Augmented Cognition International, held in conjunction with HCI International 2007.
Abstract:
Office of Naval Research (ONR) initiatives such as Human Performance Training and Education (HPT&E) as well as Virtual Technologies and Environments (VIRTE) have primarily focused on developing the strategies and technologies for creating multimodal reality or simulation based content. Resulting state-of-the-art training and education prototype simulators still rely heavily on instructors to interpret performance data, and adapt instruction via scenario generation, mitigations, feedback and after action review tools. Further research is required to fully close the loop and provide automated, adaptive instruction in these learning environments. To meet this goal, an ONR funded initiative focusing on the Training and Education arm of the HPT&E program will address the processes and components required to deliver these capabilities in the form of an Adaptive Instructional Architecture (AIA). An overview of the AIA as it applies to Marine Corps Warfighter training protocols is given as well as the theoretical foundations supporting it. Trait arousability: An individual difference that may actually be useful for designers
Schatz, S.L., Bowers, C.A., Nicholson, D., Vogel-Walcutt, J.J., "Trait arousability: An individual difference that may actually be useful for designers", Paper presented at the 2nd International Conference on Applied Ergonomics.
Abstract:
In this paper we describe trait arousability, an individual variation in how people react to high-information stimuli. Arousable individuals are particularly sensitive to stimuli, and consequently, experience high arousal states more often and for longer durations. Since arousal impacts many cognitive processes (e.g., attention, anxiety, memory, stress, and motivation), variations in arousability meaningfully impact many downstream events (including overall performance). Consequently, we encourage designers to consider this individual difference, and we offer preliminary suggestions on how to best measure, and design for, this phenomenon.
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