| Project/Area Number |
18K02910
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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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 | Hosei University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
寺脇 由紀 法政大学, 経営学部, 講師 (30559365)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | MOOCs / 学習データ解析 / 深層学習 / 機械学習 / マルチモーダル / 成績予測 / 教育工学 / 学習解析 / 大規模データ解析 |
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| Outline of Final Research Achievements |
Recent advancements in artificial intelligence technology have enabled the use of machine learning in the field of learning data analysis allowing for reliable predictions of performance evaluation. In this research project, various machine learning and deep learning techniques were employed to improve the predictive accuracy. In the latter part, we also incorporated new technologies and conducted experiments and researches on XR (cross reality) and multi-modal learning data analysis. Through frequent discussions with overseas researchers in the same field, we would like to incorporate cutting-edge technologies into our research. We will continue our research while continuously incorporating new technologies.
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| Academic Significance and Societal Importance of the Research Achievements |
最先端の人工知能技術を用いた新しい学習データ解析技術について研究した。学習において目標となる学習モデルに対して、実際の学習状況をデータとして取得し、その段階での評価と、将来の評価を高い確率で予測することができた。さらに、本研究課題では、さまざまな機械学習技術を用いた成績の予測確率の比較を行った。
後半では，新しいアプローチとしてXR（クロスリアリティ）空間での学習環境を用いて同様の学習データ解析に関する研究を行った。
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