| Project/Area Number |
26350357
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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 |
Educational technology
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| Research Institution | The National Center for University Entrance Examinations |
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Principal Investigator |
Ishioka Tsunenori 独立行政法人大学入試センター, 研究開発部, 教授 (80311166)
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| Co-Investigator(Kenkyū-buntansha) |
峯 恒憲 九州大学, システム情報科学研究院, 准教授 (30243851)
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| Research Collaborator |
KAMEDA Masayuki 大学入試センター, 研究開発部付, 学術支援員
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 情報システム / 自然言語処理 / 機械学習 / CBT / Webアプリケーション / 自動評価・自動採点 / 人工知能 / 共通テスト / 高大接続システム / 統計科学 / 教育工学 / 統計数学 |
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| Outline of Final Research Achievements |
We have developed an automated Japanese short-answer scoring and support machine for new National Center Test written exams. Our approach is based on the fact that recognizing textual entailments and/or synonymy has been almost impossible for scoring systems for several years. The system generates automated scores on the basis of evaluation criteria or rubrics, and human raters revise the scores. The system determines semantic similarity between the model answers and the actual written answers as well as a certain degree of semantic identity and implication. Owing to the need for the scoring results to be classified at multiple levels, we use random forests to effectively utilize many predictors rather than use support vector machines. An experimental prototype operates as a web system on a Linux computer.
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