Microcomputer Intelligence for Technical Training (MITT): The evolution of an intelligent tutoring system

Microcomputer Intelligence for Technical Training (MITT) uses Intelligent Tutoring System (OTS) technology to deliver diagnostic training in a variety of complex technical domains. Over the past six years, MITT technology has been used to develop training systems for nuclear power plant diesel generator diagnosis, Space Shuttle fuel cell diagnosis, and message processing diagnosis for the Minuteman missile. Presented here is an overview of the MITT system, describing the evolution of the MITT software and the benefits of using the MITT system

MITT writer and MITT writer advanced development: Developing authoring and training systems for complex technical domains

MITT Writer is a software system for developing computer based training for complex technical domains. A training system produced by MITT Writer allows a student to learn and practice troubleshooting and diagnostic skills. The MITT (Microcomputer Intelligence for Technical Training) architecture is a reasonable approach to simulation based diagnostic training. MITT delivers training on available computing equipment, delivers challenging training and simulation scenarios, and has economical development and maintenance costs. A 15 month effort was undertaken in which the MITT Writer system was developed. A workshop was also conducted to train instructors in how to use MITT Writer. Earlier versions were used to develop an Intelligent Tutoring System for troubleshooting the Minuteman Missile Message Processing System

Advanced Technology Training System on Motor-Operated Valves

This paper describes how features from the field of Intelligent Tutoring Systems are applied to the Motor-Operated Valve (MOV) Advanced Technology Training System (ATTS). The MOV ATTS is a training system developed at Galaxy Scientific Corporation for the Central Research Institute of Electric Power Industry in Japan and the Electric Power Research Institute in the United States. The MOV ATTS combines traditional computer-based training approaches with system simulation, integrated expert systems, and student and expert modeling. The primary goal of the MOV ATTS is to reduce human errors that occur during MOV overhaul and repair. The MOV ATTS addresses this goal by providing basic operational information of the MOV, simulating MOV operation, providing troubleshooting practice of MOV failures, and tailoring this training to the needs of each individual student. The MOV ATTS integrates multiple expert models (functional and procedural) to provide advice and feedback to students. The integration also provides expert model validation support to developers. Student modeling is supported by two separate student models: one model registers and updates the student's current knowledge of basic MOV information, while another model logs the student's actions and errors during troubleshooting exercises. These two models are used to provide tailored feedback to the student during the MOV course

Scaling up the diversity-resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire.

Understanding the mechanisms underlying ecosystem resilience - why some systems have an irreversible response to disturbances while others recover - is critical for conserving biodiversity and ecosystem function in the face of global change. Despite the widespread acceptance of a positive relationship between biodiversity and resilience, empirical evidence for this relationship remains fairly limited in scope and localized in scale. Assessing resilience at the large landscape and regional scales most relevant to land management and conservation practices has been limited by the ability to measure both diversity and resilience over large spatial scales. Here, we combined tools used in large-scale studies of biodiversity (remote sensing and trait databases) with theoretical advances developed from small-scale experiments to ask whether the functional diversity within a range of woodland and forest ecosystems influences the recovery of productivity after wildfires across the four-corner region of the United States. We additionally asked how environmental variation (topography, macroclimate) across this geographic region influences such resilience, either directly or indirectly via changes in functional diversity. Using path analysis, we found that functional diversity in regeneration traits (fire tolerance, fire resistance, resprout ability) was a stronger predictor of the recovery of productivity after wildfire than the functional diversity of seed mass or species richness. Moreover, slope, elevation, and aspect either directly or indirectly influenced the recovery of productivity, likely via their effect on microclimate, while macroclimate had no direct or indirect effects. Our study provides some of the first direct empirical evidence for functional diversity increasing resilience at large spatial scales. Our approach highlights the power of combining theory based on local-scale studies with tools used in studies at large spatial scales and trait databases to understand pressing environmental issues

Scaling up the diversity-resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire.

Understanding the mechanisms underlying ecosystem resilience - why some systems have an irreversible response to disturbances while others recover - is critical for conserving biodiversity and ecosystem function in the face of global change. Despite the widespread acceptance of a positive relationship between biodiversity and resilience, empirical evidence for this relationship remains fairly limited in scope and localized in scale. Assessing resilience at the large landscape and regional scales most relevant to land management and conservation practices has been limited by the ability to measure both diversity and resilience over large spatial scales. Here, we combined tools used in large-scale studies of biodiversity (remote sensing and trait databases) with theoretical advances developed from small-scale experiments to ask whether the functional diversity within a range of woodland and forest ecosystems influences the recovery of productivity after wildfires across the four-corner region of the United States. We additionally asked how environmental variation (topography, macroclimate) across this geographic region influences such resilience, either directly or indirectly via changes in functional diversity. Using path analysis, we found that functional diversity in regeneration traits (fire tolerance, fire resistance, resprout ability) was a stronger predictor of the recovery of productivity after wildfire than the functional diversity of seed mass or species richness. Moreover, slope, elevation, and aspect either directly or indirectly influenced the recovery of productivity, likely via their effect on microclimate, while macroclimate had no direct or indirect effects. Our study provides some of the first direct empirical evidence for functional diversity increasing resilience at large spatial scales. Our approach highlights the power of combining theory based on local-scale studies with tools used in studies at large spatial scales and trait databases to understand pressing environmental issues