| 研究課題/領域番号 |
21K19824
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| 研究種目 |
挑戦的研究(萌芽)
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| 配分区分 | 基金 |
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| 審査区分 |
中区分62:応用情報学およびその関連分野
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| 研究機関 | 京都大学 |
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研究代表者 |
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| 研究分担者 |
久富 望 京都大学, 教育学研究科, 助教 (70825992)
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| 研究期間 (年度) |
2021-07-09 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
6,370千円 (直接経費: 4,900千円、間接経費: 1,470千円)
2022年度: 2,600千円 (直接経費: 2,000千円、間接経費: 600千円)
2021年度: 3,770千円 (直接経費: 2,900千円、間接経費: 870千円)
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| キーワード | self-explanation / real-time feedback / data generation / automated scoring / Modality analysis / Learning process / Learning Analytics / Recommendation / Knowledge Map / Modality Analysis / Learning Process |
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| 研究開始時の研究の概要 |
Learners often get stuck or fail a task during the process of learning a new skill or knowledge. This research investigates a novel analysis method from multi source data of learner modality combined with reading behavior and knowledge mapping to predict the plateau in the mastery of prerequisites.
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| 研究実績の概要 |
The self-explanation real-time feedback and recommender system was developed, and an experiment was conducted in a school to determine it's usefulness. The system focused on providing timely feedback to students who has completed the self-explanation task. The results of this were publised as an article in an international journal. However, some additional problems were found during the development and evaluation, namely: the lack of data for traning feedback and scoring models, and issues with being able to provide sample self-explanations to students as feedback. We investigated using LLMs to generate additional datasets and found that this could enhance the traininng and accuracy of self-explanation scoring models, and disseminated these as articles in an international journal.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
Additional issues were found when evaluating a self-explanation real-time feedback system, and this has widened our investigation to include data generation and sample self-explanation example generation, which has extended the overall duration of the research project.
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| 今後の研究の推進方策 |
The results of the real-time self-explanation feedback system has also bought about data issues that have been examined to an extent, and this will be used to revised the feedback and scoring system. We plan to conduct an additional evaluation on this and anticipate in writing several journal and international conference papers to disseminate the findings to the broader research community.
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