電気学会論文誌C(電子・情報・システム部門誌)
Online ISSN : 1348-8155
Print ISSN : 0385-4221
ISSN-L : 0385-4221
<ソフトコンピューティング・学習>
Quantification of the Depth of Student Learning in Group Discussions to Support Active Learning Using Revised Taxonomy
Asako OhnoYuka NakagawaYoshiro Imai
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ジャーナル 認証あり

2022 年 142 巻 3 号 p. 382-388

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The Ministry of Education, Culture, Sports, Science and Technology (MEXT) has called for educational institutions to realize “independent, interactive, and deep learning” in order to develop the qualities and abilities necessary for children to live in the human-centered society (Society 5.0) that Japan aims to establish in the future. As a result, active learning, such as group discussions, is becoming more common in school education. However, quantitative evaluation indicators and methods have not been established to show whether “deep learning” has been achieved or not. In this study, we propose a method to quantify the depth of learning based on the features extracted from the content of the students' comments in group discussions using the Revised Taxonomy, and to present the results to the teacher.

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© 2022 by the Institute of Electrical Engineers of Japan
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