Presentation 2019-11-09
A Method for Determining Web News Suitable for NIE in Elementary Schools
Shinya Seki, Kazuaki Ando,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) NIE (Newspaper in Education) is an educational approach that utilizes newspaper as teaching materials in classes. It is being implemented in elementary schools and junior high schools mainly. However, teachers’ workloads have increased by choosing suitable articles and preparing support materials for NIE, in order to conduct NIE classes. The purpose of this study is to construct a Web news recommendation system for elementary school teachers. This paper proposes a method for determining Web news suitable as teaching materials of NIE in elementary schools, using Support Vector Machine (SVM) based on features of NIE worksheets and news articles.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Newspaper in Education / Text Classfication / News Recommendation / Machine Leanrinig / Support for Teachers / Web News / Support Vector Machine
Paper # ET2019-50
Date of Issue 2019-11-02 (ET)

Conference Information
Committee ET
Conference Date 2019/11/9(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Hiroshima City University
Topics (in Japanese) (See Japanese page)
Topics (in English) Learning Support Using VR-AR, etc.
Chair Hideyuki Suzuki(Ibaraki Univ.)
Vice Chair Ryo Takaoka(Yamaguchi Univ.)
Secretary Ryo Takaoka(Waseda Univ.)
Assistant Megumi Kurayama(National Inst. of Tech., Hakodate College) / Ryo Oonuma(Fukushima Univ.)

Paper Information
Registration To Technical Committee on Educational Technology
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Method for Determining Web News Suitable for NIE in Elementary Schools
Sub Title (in English)
Keyword(1) Newspaper in Education
Keyword(2) Text Classfication
Keyword(3) News Recommendation
Keyword(4) Machine Leanrinig
Keyword(5) Support for Teachers
Keyword(6) Web News
Keyword(7) Support Vector Machine
1st Author's Name Shinya Seki
1st Author's Affiliation Kagawa University(Kagawa Univ)
2nd Author's Name Kazuaki Ando
2nd Author's Affiliation Kagawa University(Kagawa Univ)
Date 2019-11-09
Paper # ET2019-50
Volume (vol) vol.119
Number (no) ET-276
Page pp.pp.17-20(ET),
#Pages 4
Date of Issue 2019-11-02 (ET)