| Project/Area Number |
24242017
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Foreign language education
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| Research Institution | Tokyo University of Foreign Studies |
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Principal Investigator |
Tono Yukio 東京外国語大学, 大学院総合国際学研究院, 教授 (10211393)
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| Co-Investigator(Kenkyū-buntansha) |
NEGISHI MASASHI 東京外国語大学, 大学院総合国際学研究院, 教授 (50189362)
AIKAWA MASAO 京都外国語大学, 外国語学部, 教授 (60290467)
TERAUCHI HAJIME 高千穂大学, 商学部, 教授 (50307146)
NAKATANI YASUO 法政大学, 経済学部, 教授 (90290626)
OKUMURA MANABU 東京工業大学, 精密工学研究所, 教授 (60214079)
KANEKO EMIKO 会津大学, コンピュータ理工学部, 教授 (30533624)
NOTOHARA YOSHIYUKI 同志社大学, 文学部, 准教授 (70300613)
ISHII YASUTAKE 成城大学, 社会イノベーション学部, 准教授 (70530103)
UCHIDA SATORU 九州大学, 言語文化研究院, 准教授 (20589254)
IZUMI EMI 同志社大学, 全学共通教養教育センター, 准教授 (80450691)
大羽 良 中央大学, 経済学部, 准教授 (10308158)
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| Project Period (FY) |
2012-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥45,110,000 (Direct Cost: ¥34,700,000、Indirect Cost: ¥10,410,000)
Fiscal Year 2015: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2014: ¥11,180,000 (Direct Cost: ¥8,600,000、Indirect Cost: ¥2,580,000)
Fiscal Year 2013: ¥10,400,000 (Direct Cost: ¥8,000,000、Indirect Cost: ¥2,400,000)
Fiscal Year 2012: ¥11,830,000 (Direct Cost: ¥9,100,000、Indirect Cost: ¥2,730,000)
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| Keywords | 英語到達度指標 / CEFR / 学習者コーパス / コーパス言語学 / 第二言語習得 / CEFRレベル別基準特性 / 学習者プロファイリング / 言語テスト / 外国語能力到達度評価 / 機械学習 / CEFR レベル別基準特性 |
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| Outline of Final Research Achievements |
The present study aims to propose a common framework of reference for English language teaching and learning in Japan as well as a set of language specifications (vocabulary, grammar and texts) for each level of the framework. For the common framework, the CEFR-J, an adapted version of the Common European Framework of Reference for Languages (CEFR), is used.
Two types of corpora, a corpus of CEFR-based course books and CEFR-classified learner corpora were used to extract grammatical, textual, and error features from each CEFR(-J) level. The frequency data were then analyzed using a machine learning technique called Support Vector Machine to learn how they serve to contribute to an effective classification of CEFR levels. Important features were then made into a set of inventories as reference level descriptions, called the Grammar Profile, the Text Profile and the Error Profile, which will be made publicly available for developing syllabuses, textbooks, and language tests.
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