| Project/Area Number |
22K00792
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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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| Review Section |
Basic Section 02100:Foreign language education-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
Blake John 会津大学, コンピュータ理工学部, 教授 (80635954)
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| Co-Investigator(Kenkyū-buntansha) |
Pyshkin Evgeny 会津大学, コンピュータ理工学部, 上級准教授 (50794088)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Completed (Fiscal Year 2024)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | Intelligent CALL / trend description / time-series description / describing trends / time series descriptions / written descriptions / scientific writing / academic writing / education technology / NLG / trend descriptions / describing graphs / intelligent CALL / data series description / NLP / language generation |
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| Outline of Research at the Start |
This interactive online tool provides unlimited practice opportunities to describe graphs and charts. Students can practice at three levels: word, clause or sentence using generated practice texts. Students either fill in the gaps, complete sentence stems or draft the whole text. On completion of their practice task, they compare their answers with an automatically generated plain or colorized exemplar text. This helps learners notice patterns, which is said to be a precursor to learning.
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| Outline of Final Research Achievements |
We developed TrendScribe, an online tool that helps learners of English describe trends in graphs;an important skill for university admission and academic success. Users can upload data and receive model texts at six different language levels, from beginner to advanced. The system uses both rule-based methods and artificial intelligence (AI) to generate examples. Learners can also practice by comparing their own writing to the model texts. The tool was tested with university students, who reported it was helpful and easy to use. This research combines language learning and technology to support students preparing for tests like IELTS and TOEFL. Our work helps make data interpretation and academic writing more accessible to non-native English speakers.
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| Academic Significance and Societal Importance of the Research Achievements |
Scientifically, this research contributes to natural language processing and language education. Socially, it supports inclusive learning by helping non-native speakers develop the skills needed to succeed in global academic and professional settings.
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