| 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 |
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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 |
Completed (Fiscal Year 2023)
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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 / Learning mastery / Smart Learning Systems / knowledge extraction / knowledge recommendation / human-in-the-loop system / automated labeling / Educational Data Mining / 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 Final Research Achievements |
Knowledge is an integral part of education, however many modern digital learning systems don't explicitly integrate knowledge into the learning analytics process. This research constructed fundamental infrastructure to automatically extract and simplify knowledge maps from learning contents that have been uploaded to a e-book reading system and link it to both the learning behavior data analysis and the contents from the system. A method of storing and linking the knowledge maps and learning behavior data was developed, and this was used to construct a stakeholder facing dashboard that provides knowledge maps augmented with the analysis of learning behavior data to indicate knowledge mastery and reading completion. This system constructed in this project was then used to develop a knowledge map-informed group formation process based on a genetic algorithm.
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| Academic Significance and Societal Importance of the Research Achievements |
As knowledge is an integral part of education, the achievements of this research can bring meaning to learning behavior data analysis. This also closes the gap between analysis and action by directly linking back to learning materials and test within the results visualization.
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