Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area