Intelligent tutoring systems research in the training systems division: Space applications

Computer-Aided Instruction (CAI) is a mature technology used to teach students in a wide variety of domains. The introduction of Artificial Intelligence (AI) technology of the field of CAI has prompted research and development efforts in an area known as Intelligent Computer-Aided Instruction (ICAI). In some cases, ICAI has been touted as a revolutionary alternative to traditional CAI. With the advent of powerful, inexpensive school computers, ICAI is emerging as a potential rival to CAI. In contrast to this, one may conceive of Computer-Based Training (CBT) systems as lying along a continuum which runs from CAI to ICAI. Although the key difference between the two is intelligence, there is not commonly accepted definition of what constitutes an intelligent instructional system

Spacecraft software training needs assessment research, appendices

The appendices to the previously reported study are presented: statistical data from task rating worksheets; SSD references; survey forms; fourth generation language, a powerful, long-term solution to maintenance cost; task list; methodology; SwRI's instructional systems development model; relevant research; and references

Development of Techniques to Perform Simulation-Adaptation in a Simulation Training Environment Using Expert System Methods

The use of computers for instructional purposes is steadily increasing, along with an emphasis on developing systems which create environments tailored to human beings. Artificial Intelligence techniques have been incorporated into these systems with an aim at developing better methods of modeling of simulating knowledge and intelligent behavior. One type of these systems, Intelligent Simulation Training Systems (ISTS), utilize a simulation in the training process. This is an ideal environment for the instruction of skills which focus on the ability to understand the time and space relationships of objects. An intelligent tutor module of an ISTS must configure scenarios for the simulation which meet the objectives of the student\u27s current lesson. This document describes research efforts aimed at designing and implementing methods in which a tutor module intelligently configures scenarios off-line and then dynamically adapts these scenarios on-line as required, within the simulation

Adaptive Guidance: Enhancing Self-Regulation, Knowledge, and Performance in Technology-Based Training

Considerable research has examined the effects of giving trainees control over their learning (Steinberg, 1977, 1989; Williams, 1993). The most consistent finding of this research has been that trainees do not make good instructional use of the control they are given. Yet, today’s technologically based training systems often provide individuals with significant control over their learning (Brown, 2001). This creates a dilemma that must be addressed if technology is going to be used to create more effective training systems. The current study extended past research that has examined the effects of providing trainees with some form of advisement or guidance in addition to learner control and examined the impact of an instructional strategy, adaptive guidance, on learning and performance in a complex training environment. Overall, it was found that adaptive guidance had a substantial effect on the nature of trainees’ study and practice, self-regulation, knowledge acquired, and performance

A Review of Training Methods and Instructional Techniques: Implications for Behavioral Skills Training in U.S. Astronauts (DRAFT)

Long-duration space missions (LDM) place unique physical, environmental and psychological demands on crewmembers that directly affect their ability to live and work in space. A growing body of research on crews working for extended periods in isolated, confined environments reveals the existence of psychological and performance problems in varying degrees of magnitude. The research has also demonstrated that although the environment plays a cathartic role, many of these problems are due to interpersonal frictions (Wood, Lugg, Hysong, & Harm, 1999), and affect each individual differently. Consequently, crewmembers often turn to maladaptive behaviors as coping mechanisms, resulting in decreased productivity and psychological discomfort. From this body of research, critical skills have been identified that can help a crewmember better navigate the psychological challenges of long duration space flight. Although most people lack several of these skills, most of them can be learned; thus, a training program can be designed to teach crewmembers effective leadership, teamwork, and self-care strategies that will help minimize the emergence of maladaptive behaviors. Thus, it is the purpose of this report is twofold: 1) To review the training literature to help determine the optimal instructional methods to use in delivering psychological skill training to the U.S. Astronaut Expedition Corps, and 2) To detail the structure and content of the proposed Astronaut Expedition Corps Psychological Training Program

Voice input/output capabilities at Perception Technology Corporation

Condensed resumes of key company personnel at the Perception Technology Corporation are presented. The staff possesses recognition, speech synthesis, speaker authentication, and language identification. Hardware and software engineers' capabilities are included

Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data

Learning individualized treatment regimes (ITR) using observational data holds great interest in various fields, as treatment recommendations based on individual characteristics may improve individual treatment benefits with a reduced cost. It has long been observed that different individuals may respond to a certain treatment with significant heterogeneity. ITR can be defined as a mapping between individual characteristics to a treatment assignment. The optimal ITR is the treatment assignment that maximizes expected individual treatment effects. Rooted from personalized medicine, many studies and applications of ITR are in medical fields and clinical practice. Heterogeneous responses are also well documented in educational interventions. However, unlike the efficacy study in medical studies, educational interventions are often not randomized. Study results often suffer greatly from self-selection bias. Besides the intervention itself, the efficacy and effectiveness of interventions usually interact with a wide range of confounders. In this study, we propose a novel algorithm to extend random forest of interaction trees to Casual Effect Random Forest of Interaction Trees (CERFIT) for learning individualized treatment effects and regimes. We first consider the study under a binary treatment setting. Each interaction tree recursively partitions the data into two subgroups with greatest heterogeneity of treatment effect. By integrating propensity score into the tree growing process, subgroups from the proposed CERFIT not only have maximized treatment effect differences, but also similar baseline covariates. Thus it allows for the estimation of the individualized treatment effects using observational data. In addition, we also propose to use residuals from linear models instead of the original responses in the algorithm. By doing so, the numerical stability of the algorithm is greatly improved, which leads to an improved prediction accuracy. We then consider the learning problem under non-binary treatment settings. For multiple treatments, through recursively partitioning data into two subgroups with greatest treatment effects heterogeneity with respect to two randomly selected treatment groups, the algorithm transforms the multiple learning ITR into a binary task. Similarly, continuous treatment can be handled through recursively partitioning the data into subgroups with greatest homogeneity in terms of the association between the response and the treatment within a child node. For all treatment settings, the CERFIT provides variable importance ranking in terms of treatment effects. Extensive simulation studies for assessing estimation accuracy and variable importance ranking are presented. CERFIT demonstrates competitive performance among all competing methods in simulation studies. The methods are also illustrated through an assessment of a voluntary education intervention for binary treatment setting and learning optimal ITR among multiple interventions for non-binary treatments using data from a large public university

An Examination of Novice and Expert Teachers\u27 Pedagogy in a Mixed-Reality Simulated Inclusive Secondary Classroom Including a Student Avatar With Autism Spectrum Disorders

Teachers, special and general educators alike, are required to teach a variety of students including students with ASD. With a rise in the prevalence of autism by 119.4% since 2000 (Centers for Disease Control and Prevention [CDC], 2016) and 39% of students with ASD being served in general education classrooms for over 80% of the school day (U.S. Department of Education, 2015), teachers need to be prepared to effectively teach this population. To better prepare teachers, the researcher conducted a two-phase study, situated in the framework of the Skill Acquisition Model (Dreyfus & Dreyfus, 1986) to explore the behaviors of novice and expert teachers in a simulated secondary inclusive environment. This classroom included a virtual student with autism. In phase one, the researcher conducted a Delphi Study to determine the best practices, perceived by experts in the field, for teachers who serve students with ASD in inclusive secondary environments. During phase two, the researcher used the list of skills identified as a framework to observe and interview 10 teachers, five novices and five experts, in a simulated secondary inclusive environment. The researcher identified 11 high leverage simulation practices (HLSP) that expert teachers should use while teaching in a simulated secondary inclusive environment. Observations and reflections of expert and novice teachers were analyzed, finding only 4 HLSP among experts and 5 HLSP among novice teachers. Additional HLSP were seen through the teachers\u27 reflections. Data were analyzed and discussed in detail. Implications for practice and recommendations for future research in teacher preparation is provided