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Title: A web-based intelligent learning environment for the teaching of industrial continuous quality improvement
Author: Chi, Xuesong
ISNI:       0000 0001 3543 5918
Awarding Body: University of Greenwich
Current Institution: University of Greenwich
Date of Award: 2008
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This thesis presents a methodology for developing an intelligent platform for continuous quality improvement, in order to deliver an efficient learning environment for students to learn quality improvement techniques in a structured manner. Many quality improvement programmes often fail because these techniques and their applications are not understood in a specific domain. The proposed methodology helps students identify the fundamental link between theory and realistic systems, as well as providing educators with an effective technique for teaching continuous quality improvement. A prototype system for the web-based learning environment is described, demonstrating the implementation of the methodology, and the development of intrinsic links between a virtual learning environment and real systems. Through tests carried out during two quality engineering courses, the study demonstrates that students are immersed and motivated in the web-based virtual environments through a game-based learning paradigm with positive results. By extending the prototype modules, the capability of the proposed system to balance the relationship between quality, productivity and cost is highlighted. This delivers a holistic and multidimensional approach for quality engineering courses and training, with the opportunities to extend the benefits of the virtual learning environment to other areas of expertise, such as operations and supply chain management. This study also explores the importance of capturing the dynamic characteristics of a real system and representing it within a virtual learning environment which aims to provide a realistic experience to its users. Two artificial neural network modules (a Fuzzy Adaptive Resonance Theory neural network and a back-propagation neural network) are implemented to facilitate the understanding of statistical tools and different types of variation in a realistic process.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA75 Electronic computers. Computer science