2003 Fiscal Year Final Research Report Summary
Research on development of retrieval and classification system of documents on the Web which is based on analysis of teaching text books.
| Project/Area Number |
13558012
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Educational technology
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| Research Institution | TOKYO INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
NAKAYAMA Minoru TOKYO INSTITUTE OF TECHNOLOGY, THE CENTER FOR RESEACH AND DEVELOPMENT OF EDUCATIONAL TECHNOLOGY, Assoc.Professor, 教育工学開発センター, 助教授 (40221460)
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| Co-Investigator(Kenkyū-buntansha) |
MUROTA Masao TOKYO INSTITUTE OF TECHNOLOGY, GRADUATE SCHOOL OF DECISION SCIENCE AND TECHNOLOGY, Assoc. Professor, 大学院・社会理工学研究科, 助教授 (30222342)
NISHIKATA Atsuhiro TOKYO INSTITUTE OF TECHNOLOGY, THE CENTER FOR RESEACH AND DEVELOPMENT OF EDUCATIONAL TECHNOLOGY, Assoc. Professor, 教育工学開発センター, 助教授 (60260535)
SHIMIZU Yasutaka NATIONAL INSTITUTE OF EDUCATIONAL POLICY RESEARCH, CENTER FOR EDUCATIONAL RESOURCES, Director, 教育研究情報センター長 (10016561)
TAKAHIRO Aoyagi TOKYO INSTITUTE OF TECHNOLOGY, HE CENTER FOR RESEACH AND DEVELOPMENT OF EDUCATIONAL TECHNOLOGY, Research Assoiciate, 教育工学開発センター, 助手 (10302944)
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| Project Period (FY) |
2001 – 2003
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| Keywords | educational information / web resources / Document classfication / Self-Organizing Map / Multi Layer Perceptron / Vector Space Model / Singular Value Decomposition / Information Retrieval |
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| Research Abstract |
The purpose of this study is to compare performance among developed subject classification methods for educational Web re-sources.
Three methods are vector space model (VSM) using degree of cosine similarity, vector distance on the self-organizing map (SOM) and three layer percptron (MLP). The document vectors were estimated by the term feature vectors which were extracted from the teaching guidelines based on the singular value decomposition method (SVD).
The 403 teaching schemes were prepared as the test documents for the subject discrimination. The recall rate, precision rate and Fl measure as the classification performance measure, were compared across three methods. The recall rate using MLP is the highest for most subjects. The highest precision rate among the methods depend on subject. As a result, F1 measure as total performance index of most subjects are the highest for the method using MLP.
According to the results, the subject classification methods for educational Web resources by using multi layer perceptron (MLP) is tested to achieye better performance. Ten classification methods and 403 test documents were prepared, then classification perfor-mance was compared among conditions by the F1 measure.
As a result, it was examined effectiveness of singular value decomposition (SVD) to extract term feature from term-document matrix as description characteristics of the national teaching guideline. The highest performance of classification method was the use of MLP which trained with all term features extracting from SVD when document feature was estimated by terms appering unique subject.
Also, it was suggested that there were significant differences of document feature components between correct and incorrect classified documents.
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Research Products
(13 results)
