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Authors: Seiji Saito and Takenobu Takizawa

Affiliation: Waseda University, Japan

ISBN: 978-989-758-201-1

Keyword(s): Fuzzy Graph, Fuzzy Clustering, Fuzzy Number, Fuzzy Cognition Graph.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Fuzzy Systems ; Soft Computing ; Type-2 Fuzzy Logic

Abstract: Applying fuzzy clustering method to the instruction structure analysis, we can investigate whether the order of teaching item is suitable or not. However, when the teacher gives learners partial points, it is difficult to judge whether the leaner solve the problem correctly or not. In this paper, the authors regard the score of the test as the fuzzy number, and present a new analysis method using fuzzy number. We show some graphs required for analysis based on the results of examination for high school students and represent the effectivity of the method.

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Paper citation in several formats:
Saito, S. and Takizawa, T. (2016). Instruction Structure Analysis Appling Fuzzy Number.In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 88-92. DOI: 10.5220/0006050200880092

@conference{fcta16,
author={Seiji Saito. and Takenobu Takizawa.},
title={Instruction Structure Analysis Appling Fuzzy Number},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2016)},
year={2016},
pages={88-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006050200880092},
isbn={978-989-758-201-1},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2016)
TI - Instruction Structure Analysis Appling Fuzzy Number
SN - 978-989-758-201-1
AU - Saito, S.
AU - Takizawa, T.
PY - 2016
SP - 88
EP - 92
DO - 10.5220/0006050200880092

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