1995 Fiscal Year Final Research Report Summary
Baysian Evaluation System based on Teachers 'Wisdom Using Network Model
| Project/Area Number |
06451142
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Educational technology
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| Research Institution | The University of Tokyo (1995)
Tokyo Institute of Technology (1994) |
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Principal Investigator |
SHIGEMASU Kazuo The University of Tokyo, College of Arts and Sciences, Professor, 教養学部, 教授 (90091701)
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| Co-Investigator(Kenkyū-buntansha) |
UENO Maomi Chiba University, Faculty of Literature, Research Associate, 文学部, 助手 (50262316)
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| Project Period (FY) |
1994 – 1995
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| Keywords | hierarchical modeling / Baysian analysis / Gibbs sampler / latent variables / bug rule / medical diagnosis / Item Response theory |
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| Research Abstract |
(1) The Baysian Theory of Hierarchical Modeling was developed to analyze the network model. Also, the suitable analysis method for the hierarchical method was developed using the Gibbs Sampler. In order to analyze the internal process of the leaner, the complex modeling is necessary, but the available data is often categorical data. Therefore, we need the hierarchical model which includes the distributions of latent variables and the prior distributions. Also, the Gibbs sampling is a very useful tool to obtain the reliable estimates of parameters.
(2) A Baysian network model was developed to identify learners' bug rules and more generally learner's status in the learning map. This system calculates the posterior probability for each of the possible bug rules. Also, as another application, the medical network diagnosis system which includes the continuous variables as nodes was developed. This system can be modified easily to handle the educational data.
(3) An Item Response Model for the polychotomous responses was developed to obtain useful information for teachers. This analysis can discriminates the ability parameters of the same score, and can provide the more detailed information for each of test items.
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