| Project/Area Number |
19K12167
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 61050:Intelligent robotics-related
|
|---|
| Research Institution | Tokyo University of Agriculture and Technology |
|---|
Principal Investigator |
Venture Gentiane 東京農工大学, 工学(系)研究科(研究院), 客員教授 (30538278)
|
|---|
| Project Period (FY) |
2019-04-01 – 2022-03-31
|
| Project Status |
Completed (Fiscal Year 2021)
|
| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
|---|
| Keywords | ロボティクス / 教育支援 / Machine learning / Human behavior analysis / online teaching / remote teaching / intelligent robot / physical avatar / 遠隔教育 |
|---|
| Outline of Research at the Start |
I will collect behavioural data of students in classrooms using motion capture technology and eye tracking technology as well as with marker-less camera base technology. Then I will develop a software tool for automated and quantified behavioural analysis of the data and create models of the
teaching styles and their relationship with students’ behaviours using big data. Finally, I will deploy the models in a social robotics virtual agent that can support online courses’ material development and teachers’ training and I will evaluate this teaching tool in real settings.
|
| Outline of Final Research Achievements |
The project explored the use of social robots to study and support the intellectual and behavioural impact of higher education. A quantitative and comparative analysis of teaching styles and students’ behaviour was conducted in university classrooms and the behaviours of an artificial agent have been developed to react in real-time.
We collected behavioural data of students in classrooms using marker-less camera base technology. Then we developed a software tool for automated and quantified behavioural analysis of the data and created models of the learning styles and their relationship with students’ behaviours using big data. Finally, we deployed the models in social robotics virtual agents that could support online courses’ material development and teachers’ training.
|
| Academic Significance and Societal Importance of the Research Achievements |
この研究は2019年に始まり、2020年にはオンライン授業が一気に一般化するパンデミックに世界が見舞われるという、非常にタイムリーなものでした。本研究の結果となされた展開は、学生の幸福を分析し支援するために最も重要なものである。プロジェクトの成果は、さらに継続されることが有益であっただろう。
|
