| Project/Area Number |
25282058
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Educational technology
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| Research Institution | Hiroshima University |
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Principal Investigator |
Yasutake Koichi 広島大学, 社会(科)学研究科, 准教授 (80263664)
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| Co-Investigator(Kenkyū-buntansha) |
中村 泰之 名古屋大学, 情報科学研究科, 准教授 (70273208)
多川 孝央 九州大学, 情報基盤研究開発センター, 助教 (70304764)
山川 修 福井県立大学, 学術教養センター, 教授 (90230325)
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| Co-Investigator(Renkei-kenkyūsha) |
SUMIYA TAKAHIRO 広島大学, 情報メディア教育研究センター, 准教授 (90231381)
INOUE HITOSHI 九州大学, 情報基盤研究開発センター, 准教授 (70232551)
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| Project Period (FY) |
2013-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥10,530,000 (Direct Cost: ¥8,100,000、Indirect Cost: ¥2,430,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2014: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2013: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
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| Keywords | 学習科学 / ラーニング・アナリティクス / 社会物理学 / 学習分析 / 教育工学 / 理論モデル / 数理モデル / 複雑系アプローチ / 解析・評価 / ネットワーク / ビッグ・データ / 協調学習 / 理論分析 / ネットワーク科学 / Learning Analytics / 複雑ネットワーク / 数理分析 / 複雑系 |
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| Outline of Final Research Achievements |
In this research, we proposed the brand-new Learning Analytics approaches, which are very different from traditional ones in the areas of Learning Sciences and/or Educational Engineering. What is important and new methodologically is that our research is mathematical and computational one. Our achievements are as follows; (1) We planed and managed a lot of research seminars, forums, discussion sessions in conferences in terms of Learning Analytics (FIT2013, JSET2013, JSiSE2014, JSiSE2014, and so on). (2) We investigated relationships between "Learners Collaborative Structures" and the effects using theoretical network simulation models. (3) We expanded our research to analyze learners activities in "micro-scopic" levels. In this research, we found that the aggregated probability distribution of learners physical rhythm
(acceleration) characterize "Power Distribution" if the learning community grow positively.
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