| Project/Area Number |
17K02860
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Japanese language education
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| Research Institution | Fukuoka Women's University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 語彙習得 / 多読 / 日本語教科書 / 話題 / 読解 / 日本語教育 / 読解教育 / 話題・トピック / コーパス / 日本語教材 / SDGs / トピック / 話題シラバス / 日本語教科書コーパス / 話題別多読 |
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| Outline of Final Research Achievements |
In this study, we proposed "topic-based extensive reading" as a method to effectively and systematically promote incidental vocabulary acquisition, and created the "Japanese Textbook Reading Materials Database" as a tool for this purpose, as well as the "Corpus of Japanese reading materials" for its validation. The "topic-based extensive reading" method is based on the idea that by reading multiple reading materials on a common topic, the occurrence of important words on that topic is guaranteed, leading to systematic vocabulary acquisition. Since Japanese language textbooks, which are materials tailored for learners, are particularly suited for topic-based extensive reading, I created a searchable database of 4200 texts from 223 Japanese language textbooks, each with a topic tag. A corpus for linguistic analysis of those texts was then created and validated.
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| Academic Significance and Societal Importance of the Research Achievements |
既存の日本語教科書を活用し、語彙習得の一つの方法である付随的語彙学習を効果的に行うための「話題別多読」を提唱、実践、検証した。またそのためのツールとして、誰もが利用可能な「日本語教育データベース」を作成するとともに、既存の日本語教科書の分析も行った。語彙学習はその要素の膨大さから、体系的、計画的に学習を進めていくことが難しいと言われるが、「話題」というキーワードで括ることにより、語彙学習の可視化を図ることも可能となった。
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