| Project/Area Number |
19K12064
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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 61020:Human interface and interaction-related
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| Research Institution | Kyushu University |
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Principal Investigator |
Okada Masaya 九州大学, 共創学部, 准教授 (10418519)
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| Co-Investigator(Kenkyū-buntansha) |
多田 昌裕 近畿大学, 理工学部, 准教授 (40418520)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2021: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2020: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | ラーニングアナリティクス / 行動情報学 / 実世界学習 / 学習方略 / 計算論 / 状況論的知能 |
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| Outline of Research at the Start |
実世界に根ざした知識を得るためには，机上での学びにとどまらず，実世界の中で行動を通して学ぶ実世界学習が重要である．このとき，学習者が，自らの学習活動を省みて「学び方」を適切に自己調整できれば，知的生産性を高める行動をとれる．したがって，本研究では，このような「学び方」の内省を促すために，「実世界における学び方」を学習棄却・再学習させるための計算論的学習方略研究を実施する．
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| Outline of Final Research Achievements |
As an alternative of desktop learning, real-world learning by behaving in the world is important to acquire knowledge derived from the real world. If a learner can properly self-regulate "how to learn" by reflecting on his/her own learning activities, he/she can take actions to increase intellectual productivity. We conducted a computational study of learning strategies, as a basis to encourage a learner to reflect on, un-learn, and re-learn his/her way of real-world learning. Our computation models can be used to estimate qualitative characteristics of learning by measuring external situation of a learner. We expect that our basic findings are used for developing next-generation learning support with artificial intelligence.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は，学習科学，行動情報学，身体性認知科学の統合によって，実世界における状況論的知能のメカニズムについて基礎的研究成果を得た．本研究は，人工知能による次世代学習支援などの応用を行う際，その基礎的知見として活用が期待される．具体的には，行動の計測・理解からの学習効果の予測技術の発展に寄与し，実世界における学習者に効果的な行動を取るよう促す学習支援技術への応用が期待される．
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