2022 Fiscal Year Final Research Report
Learner adaptive self-study support platform using context-awareness technology
| Project/Area Number |
19K20420
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 62030:Learning support system-related
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| Research Institution | University of Fukui |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | 行動認識 / 学習支援 / 深層学習 / アンサンブル学習 |
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| Outline of Final Research Achievements |
In this study, we have developed an intelligent self-directed learning support platform that is fully adaptive to the unique needs of each individual. Our research has yielded two key results.
Firstly, we have successfully developed cutting-edge technology that accurately recognizes user activities. By specializing deep learning models to human activity recognition field, we have achieved unprecedented levels of accuracy, paving the way for more advanced and effective intelligent systems.
Secondly, we have uncovered a groundbreaking insight regarding the optimal timing of learning based on user actions. Our research has conclusively demonstrated that learning is more efficient when performed in an environment that allows the user to concentrate and move at the same time, such as on a treadmill, as opposed to stationary environments.
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| Free Research Field |
知覚情報処理
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発した，行動認識技術と，学習支援に向けた新たな知見は様々な面で今後の応用が期待できる．まず，行動認識技術は学習支援のみならず，Society5.0の実現に向けたフィジカル空間の認識手法としての利活用が見込める．例えば，行動のログを自動で記録したり，ユーザの行動に応じて様々な情報提供を行うインタラクティブシステムの開発への応用が見込める．また，学習効果に対する知見は，今後革新的な学習支援システムを実現する際のエビデンスとして活用可能である．暗記学習を行う際にトレッドミルやエクササイズバイクを活用することで，容易に学習効果を高める効果が期待できる．
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