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

Multimodal teaching, learning and training in virtual reality: a review and case study

It is becoming increasingly prevalent in digital learning research to encompass an array of different meanings, spaces, processes, and teaching strategies for discerning a global perspective on constructing the student learning experience. Multimodality is an emergent phenomenon that may influence how digital learning is designed, especially when employed in highly interactive and immersive learning environments such as Virtual Reality (VR). VR environments may aid students' efforts to be active learners through consciously attending to, and reflecting on, critique leveraging reflexivity and novel meaning-making most likely to lead to a conceptual change. This paper employs eleven industrial case-studies to highlight the application of multimodal VR-based teaching and training as a pedagogically rich strategy that may be designed, mapped and visualized through distinct VR-design elements and features. The outcomes of the use cases contribute to discern in-VR multimodal teaching as an emerging discourse that couples system design-based paradigms with embodied, situated and reflective praxis in spatial, emotional and temporal VR learning environments

Intelligent Tutoring System Authoring Tools for Non-Programmers

An intelligent tutoring system (ITS) is a software application that tries to replicate the performance of a human tutor by supporting the theory of learning by doing . ITSs have been shown to improve the performance of a student in wide range of domains. Despite their benefits, ITSs have not seen widespread use due to the complexity involved in their development. Developing an ITS from scratch requires expertise in several fields including computer science, cognitive psychology and artificial intelligence. In order to decrease the skill threshold required to build ITSs, several authoring tools have been developed. In this thesis, I document several contributions to the field of intelligent tutoring in the form of extensions to an existing ITS authoring tool, research studies on authoring tool paradigms and the design of authoring tools for non-programmers in two complex domains - natural language processing and 3D game environments. The Extensible Problem Specific Tutor (xPST) is an authoring tool that helps rapidly develop model-tracing like tutors on existing interfaces such as webpages. xPST\u27s language was made more expressive with the introduction of new checktypes required for answer checking in problems belonging to domains such as geometry and statistics. A web-based authoring (WAT) tool was developed for the purpose of tutor management and deployment and to promote non-programmer authoring of ITSs. The WAT was used in a comparison study between two authoring tool paradigms - GUI based and text based, in two different problem domains - statistics and geometry. User-programming of natural language processing (NLP) in ITSs is not common with authoring toolkits. Existing NLP techniques do not offer sufficient power to non-programmers and the NLP is left to expert developers or machine learning algorithms. We attempted to address this challenge by developing a domain-independent authoring tool, ConceptGrid that is intended to help non-programmers develop ITSs that perform natural language processing. ConceptGrid has been integrated into xPST. When templates created using ConceptGrid were tested, they approached the accuracy of human instructors in scoring student responses. 3D game environments belong to another domain for which authoring tools are uncommon. Authoring game-based tutors is challenging due to the inherent domain complexity and dynamic nature of the environment. We attempt to address this challenge through the design of authoring tool that is intended to help non-programmers develop game-based ITSs

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

eXtended Reality for Education and Training

L'abstract è presente nell'allegato / the abstract is in the attachmen

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Skill-based human-robot cooperation in tele-operated path tracking

This work proposes a shared-control tele-operation framework that adapts its cooperative properties to the estimated skill level of the operator. It is hypothesized that different aspects of an operatorâ\u80\u99s performance in executing a tele-operated path tracking task can be assessed through conventional machine learning methods using motion-based and task-related features. To identify performance measures that capture motor skills linked to the studied task, an experiment is conducted where users new to tele-operation, practice towards motor skill proficiency in 7 training sessions. A set of classifiers are then learned from the acquired data and selected features, which can generate a skill profile that comprises estimations of userâ\u80\u99s various competences. Skill profiles are exploited to modify the behavior of the assistive robotic system accordingly with the objective of enhancing user experience by preventing unnecessary restriction for skilled users. A second experiment is implemented in which novice and expert users execute the path tracking on different pathways while being assisted by the robot according to their estimated skill profiles. Results validate the skill estimation method and hint at feasibility of shared-control customization in tele-operated path tracking