| Project/Area Number |
22240079
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Educational technology
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| Research Institution | Okayama University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
OHTA Nobuo 東京福祉大学, 心理学部, 教授 (80032168)
YAMADA Tsuyoshi 岡山大学, 大学院教育学研究科, 准教授 (10334252)
YOSHIDA Tetsuya 常葉大学, 教育学部, 准教授 (70323235)
MATSUDA Ken 山口大学, 大学院理工学研究科, 准教授 (10422916)
SUZUKI Wataru 宮城教育大学, 教育学部, 准教授 (60549640)
ITAGAKI Nobuya 宮城教育大学, 教育学部, 教授 (80193407)
SAKUMA Yasuyuki 福島大学, 人間発達文化学類, 教授 (90282293)
KAWASAKI Yuka 呉工業高等専門学校, 人文社会系, 准教授 (90615832)
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| Research Collaborator |
西山 めぐみ
上田 紋佳
三宅 貴久子
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| Project Period (FY) |
2010-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥47,710,000 (Direct Cost: ¥36,700,000、Indirect Cost: ¥11,010,000)
Fiscal Year 2014: ¥8,450,000 (Direct Cost: ¥6,500,000、Indirect Cost: ¥1,950,000)
Fiscal Year 2013: ¥8,970,000 (Direct Cost: ¥6,900,000、Indirect Cost: ¥2,070,000)
Fiscal Year 2012: ¥8,970,000 (Direct Cost: ¥6,900,000、Indirect Cost: ¥2,070,000)
Fiscal Year 2011: ¥10,270,000 (Direct Cost: ¥7,900,000、Indirect Cost: ¥2,370,000)
Fiscal Year 2010: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
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| Keywords | 教育ビッグデータ / Learning Analytics / 縦断的研究法 / 動的テスト法 / 潜在記憶 / e-learning / 異種通信システムの融合 / 実験計画法 / ビッグデータ / 縦断的研究 / 英語教育 / クラウド / 学習意欲 / 通信の融合 / 縦断的行動データ / 教育評価 / データベース / 通信・メディアの融合 / 縦断的データ / 語彙習得 / メディアの融合 / 分散学習・集中学習 / 教育系心理学 / 教育工学 / 可視化 / 因果推定法 / 異種メディア通信 / 心の体温計 / 紙メディア / スキャナ / 教育コンテンツ / 意欲向上 |
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| Outline of Final Research Achievements |
Our method visualizes in detail improvements in the grades of individual children (where such improvements are not obvious in the field of education), making it possible to provide evident feedback even to children with low grades to convey the message, “If you try, you can do it.” In addition, our method allows making highly accurate scientific predictions on learning. Consequently, it is possible to construct highly accurate educational big data sets that aggregate various responses from many individuals on a yearly basis. The analysis of such data longitudinally visualizes not only learning but also consciousness states, such as feelings of self-efficacy and trends toward depression. Thus, it is now possible to conduct scientific discussions based on causal data, such as the visualization of at-risk children and practical assessments of the influence of teacher guidance.
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