| 研究課題/領域番号 |
20H01722
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| 研究機関 | 京都大学 |
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研究代表者 |
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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| 研究分担者 |
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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| 研究期間 (年度) |
2020-04-01 – 2023-03-31
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| キーワード | Knowledge map / Smart learning systems / Knowledge extraction / Learning analytics / Educational Informatics |
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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
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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| 今後の研究の推進方策 |
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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