No description available.

This research was conducted on model-based reasoning and its prevalence in CSCOPE curriculum. Communications with seven CSCOPE representatives out of twenty regions revealed that CSCOPE is simply a name, not an acronym. The primary focus of CSCOPE is to impact instructional practices in the classroom to improve student performance. This report discusses the history of CSCOPE, its framework, and its exemplar lessons. It also looks at model-based reasoning, taxonomy of models, and model-eliciting activities. The research also aims to determine if the exemplar lessons in CSCOPE can be classified as model-based. / text

Thesis (Ph. D.)--Michigan State University. Dept. of Curriculum, Teaching, and Educational Policy, 2008. / Title from PDF t.p. (viewed on July 7, 2009) Includes bibliographical references (p. 105-109). Also issued in print.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Vachtsevanos, George; Committee Member: Liang, Steven; Committee Member: Michaels, Thomas; Committee Member: Vela, Patricio; Committee Member: Wardi, Yorai.

Thesi supervisor: Cesare Tinelli. Includes bibliographic references (p. 214-220).

There has been a call to reform science education to integrate scientific thinking practices, such as data interpretation and modeling, with learning content in science classrooms. This call to reform has taken place in both K-12 science education through Next Generation Science Standards and undergraduate education through AAAS initiative Vision and Change in Undergraduate Biology Education. This dissertation work examines undergraduate students' learning of multiple scientific thinking skills in a curricular format called Teaching Real data Interpretation with Models (TRIM) applied to a large-enrollment course in Cellular and Developmental Biology. In TRIM, students are provided worksheets in groups and tasked to interpret authentic biological data. Importantly, groups are tasked to relate their data interpretations to a 2D visual model representation of the relevant biological process. This dissertation work consists of two studies with the overarching question: How do students use model representations to interpret data interpretations? In the first study, we primarily describe how students learn to navigate and interpret discipline-based data representations. We found the majority of groups could construct quality written data interpretations. Qualitative coding analysis on group discourse found students relied on strategies such as decoding the data representation and noticing data patterns together to construct claims. Claims were refined through spontaneous collaborative argumentation. We also found groups used the provided model to connect their data inferences to a biological context. In the second study, we primarily target our analysis on how individual students relate their data interpretations to different modeling tasks, including student-generation of their own model drawing. I interviewed students one-on-one as they worked through TRIM-style worksheets. From iterative qualitative analysis of transcripts and collected video on hand movements, I characterize the forms of reasoning at play at the interface of data and model representations. I propose a model at the end of Study 2 describing three modes of reasoning in data abstraction into models. I found when relating between data and models, students needed to link signs in both representations to a common referent in the real-world phenomenon. Establishing this sign-referent relationship seemed to depend on bringing in outside mechanistic information about the phenomenon. Once a mechanism was established, students could fluidly move between data and model representations through mechanistic reasoning. Thus data abstraction seems to rely on mechanistic reasoning with models. The findings from this dissertation work support the feasibility of student development of multiple scientific thinking skills within a large lecture course, and provide targets for curriculum and assignment designs centered on teaching higher order reasoning skills.

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.

The use of knowledge-based systems to aid in the diagnosis of faults in physical devices has grown considerably since their introduction during the 1970s. The majority of the early knowledge-based systems incorporated shallow knowledge, which sought to define simple cause and effect relationships between a symptom and a fault, that could be encoded as a set of rules. Though such systems enjoyed much success, it was recognised that they suffered from a number of inherent limitations such as inflexibility, inadequate explanation, and difficulties of knowledge elicitation. Many of these limitations can be overcome by developing knowledge-based systems which contain deeper knowledge about the device being diagnosed. Such systems, now generally referred to as model-based systems, have shown much promise, but there has been little evidence to suggest that they have successfully made the transition from the research centre to the workplace. This thesis argues that knowledge-based systems are an appropriate tool for the diagnosis of faults in complex devices, and that both deep and shallow knowledge have their part to play in this process. More specifically this thesis demonstrates how a wide-ranging knowledge-based system for quality assurance, based upon shallow knowledge, can be developed, and implemented. The resultant system, named DIPLOMA, not only diagnoses faults, but additionally provides advice and guidance on the assembly, disassembly, testing, inspection and repair of a highly complex hydro-mechanical device. Additionally it is shown that a highly innovative modelbased system, named MIDAS, can be used to contribute to the provision of diagnostic, explanatory and training facilities for the same hydro-mechanical device. The methods of designing, coding, implementing and evaluating both systems are explored in detail. The successful implementation and evaluation of the DIPLOMA and MIDAS systems has shown that knowledge-based systems are an appropriate tool for the diagnosis of faults in complex hydro-mechanical devices, and that they make a beneficial contribution to the business performance of the host organisation. Furthermore, it has been demonstrated that the most effective and comprehensive knowledge-based approach to fault diagnosis is one which incorporates both deep and shallow knowledge, so that the distinctive advantages of each can be realised in a single application. Finally, the research has provided evidence that the model-based approach to diagnosis is highly flexible, and may, therefore, be an appropriate technique for a wide range of industrial applications.

The use of knowledge-based systems to aid in the diagnosis of faults in physical devices has grown considerably since their introduction during the 1970s. The majority of the early knowledge-based systems incorporated shallow knowledge, which sought to define simple cause and effect relationships between a symptom and a fault, that could be encoded as a set of rules. Though such systems enjoyed much success, it was recognised that they suffered from a number of inherent limitations such as inflexibility, inadequate explanation, and difficulties of knowledge elicitation. Many of these limitations can be overcome by developing knowledge-based systems which contain deeper knowledge about the device being diagnosed. Such systems, now generally referred to as model-based systems, have shown much promise, but there has been little evidence to suggest that they have successfully made the transition from the research centre to the workplace. This thesis argues that knowledge-based systems are an appropriate tool for the diagnosis of faults in complex devices, and that both deep and shallow knowledge have their part to play in this process. More specifically this thesis demonstrates how a wide-ranging knowledge-based system for quality assurance, based upon shallow knowledge, can be developed, and implemented. The resultant system, named DIPLOMA, not only diagnoses faults, but additionally provides advice and guidance on the assembly, disassembly, testing, inspection and repair of a highly complex hydro-mechanical device. Additionally it is shown that a highly innovative modelbased system, named MIDAS, can be used to contribute to the provision of diagnostic, explanatory and training facilities for the same hydro-mechanical device. The methods of designing, coding, implementing and evaluating both systems are explored in detail. The successful implementation and evaluation of the DIPLOMA and MIDAS systems has shown that knowledge-based systems are an appropriate tool for the diagnosis of faults in complex hydro-mechanical devices, and that they make a beneficial contribution to the business performance of the host organisation. Furthermore, it has been demonstrated that the most effective and comprehensive knowledge-based approach to fault diagnosis is one which incorporates both deep and shallow knowledge, so that the distinctive advantages of each can be realised in a single application. Finally, the research has provided evidence that the model-based approach to diagnosis is highly flexible, and may, therefore, be an appropriate technique for a wide range of industrial applications. / Science and Engineering Research Council, and Alvey Directorate

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-15T14:16:38Z No. of bitstreams: 1 ALEXANDRE NÓBREGA DUARTE - DISSERTAÇÃO PPGCC 2003..pdf: 1526817 bytes, checksum: dfc39cd8b1649bf64468cbe2eaefe99b (MD5) / Made available in DSpace on 2018-08-15T14:16:38Z (GMT). No. of bitstreams: 1 ALEXANDRE NÓBREGA DUARTE - DISSERTAÇÃO PPGCC 2003..pdf: 1526817 bytes, checksum: dfc39cd8b1649bf64468cbe2eaefe99b (MD5) Previous issue date: 2003-02-25 / Apresenta uma nova ferramenta para o diagnóstico automático de falhas em redes elétricas. A ferramenta utiliza uma técnica híbrida de correlação de eventos criada especialmente para ser utilizada em redes com constantes modificações de topologia. A técnica híbrida combina o raciocínio baseado em regras com o raciocínio baseado em modelos para eliminar as principais limitações do raciocínio baseado em regras. Com a ferramenta de diagnóstico foi possível validar o conhecimento dos especialistas em sistemas de transmissão de energia elétrica necessário para o diagnóstico de falhas em linhas de transmissão e construir uma base de regras para tal. A ferramenta foi testada no diagnóstico de falhas em linhas de transmissão de um dos cinco centros regionais da Companhia Hidro Elétrica do São Francisco (CHESF) e apresentou resultados satisfatórios de desempenho e precisão. / It presents a new tool for the automatic diagnosis of faults in electric networks. The toot uses a hybrid event correlation technique especially created to be used in networks with constant topological modifications. The hybrid technique combines ruJe-based reasoning with modelbased reasoning to eliminate the main limitations of rule-based reasoning. With the tool it was possible to validate the knowledge acquired from electric energy transmission systems specialists needed for the diagnosis of faults in transmission lines and to construct rules. The tool was tested in the diagnosis of faults in transmission lines of one of the five regional centers of the Companhia Hidro Elétrica do São Francisco (CHESF) and presented satisfactoiy results in terms of performance and precision.