| Project/Area Number |
20H01722
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 09070:Educational technology-related
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| Research Institution | Kyoto University |
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Principal Investigator |
Flanagan Brendan 京都大学, 学術情報メディアセンター, 特定講師 (00807612)
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| Co-Investigator(Kenkyū-buntansha) |
緒方 広明 京都大学, 学術情報メディアセンター, 教授 (30274260)
Majumdar Rwito 京都大学, 学術情報メディアセンター, 特定講師 (30823348)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2022: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2020: ¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
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| Keywords | Knowledge Map / Knowledge Extraction / Learning Analytics / Educational Data Mining / Smart Learning Systems / Knowledge map / Smart learning systems / Knowledge extraction / Learning analytics / Educational Informatics |
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| Outline of Research at the Start |
The study guidance code proposed by MEXT will standardize the representation of learning material knowledge structures, however a system to integrate teacher created materials with publisher contents and learning analytics systems is required to realize the full potential to support smart education and learning. This research investigates how meaningful analysis can be achieved by supporting the automated extraction, linking, management, and analysis of knowledge maps at scale.
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| Outline of Annual Research Achievements |
Initially, the focus was on developing the necessary fundamental infrastructure to support the proposed knowledge map based smart learning system. The preliminary collection and storage of knowledge structures and the linking of learning materials, quizzes and their related concepts was developed. A process of automatically extracting the relations of English vocabulary by the PI was used to implement a knowledge map-based learning task recommender system for extensive reading and the results were disseminated at international conferences. The development of a dashboard visualization system also began; however, the evaluation was delayed due to unforeseen complications from the covid pandemic. Learning log analysis for prediction academic performance from reading behavior was also conducted and the results were disseminated at international conferences (nominated for best full paper award) and an invited lecture at a NII symposium.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
aThe schedule of the project was affected by Covid-19, and there were unavoidable delays in development, implementation, and dissemination of research results. However, the motivation of teachers and students to utilize educational technology to overcome these difficulties has increased, and we anticipate that the output of this project will have greater impact on society as a result.
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| Strategy for Future Research Activity |
The continued development of the infrastructure to support the proposed smart learning system will be the main focus on the project, with the target of disseminating results from the development of automated analysis of learning materials, quizzes, and learning logs at international conferences and then journals. The application of the developed infrastructure to sub-themes, such as: recommendation, prediction, and increasing knowledge awareness through the visualization and explanation of results from the perspective of knowledge structures will also be pursued and it is anticipated to produce several promising results in the future.
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