Development of Individual Learning Support System Optimized by Learner Characteristic Analysis
| Project/Area Number |
17H04707
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
Learning support system
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| Research Institution | Shinshu University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥10,660,000 (Direct Cost: ¥8,200,000、Indirect Cost: ¥2,460,000)
Fiscal Year 2020: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2019: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2018: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 学習履歴データ / 個別学習 / 可視化システム / LRS / データ分析 / 学習支援システム / 学習分析 / 学習の個別最適化 / 学習特性の可視化 / アダプティブ・ラーニング / 個に応じた学習 / 個別学習支援 / 学習データ / システム開発 / 教育工学 / システム工学 / 情報システム |
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| Outline of Final Research Achievements |
The purpose of this study is to develop a learning support system that is individually optimized for teachers and students by visualizing the learning characteristics of learners from learning history data (who/ when/ where/ what/ how, etc.). The developed system visualizes the learning history data using LRS (Learning Recoard Store) compliant with xAPI(Experience API), the correctness display of the learning problem by the flag, and the difficulty level by the iridescent gradation. As a result, it was possible to display a list of the learning progress of the learners. Based on the visualized data, it was possible for teachers to understand the characteristics of learners, find learners with similar learning comprehension and progress, and suggest the possibility of supporting collaborative learning.
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| Academic Significance and Societal Importance of the Research Achievements |
学習者の理解状況把握のための学習者データ分析には，統計学やデータ解析に関する専門的な知識や技能がなければ困難であったが，本研究を通じて，専門的な知識や技能がなくとも当該学習者の学習特性を可視化することを可能とした。
また，学習者個人の反応指標等を解析し，子供に自身の成績が上昇するグラフをフィードバックすることで学習意欲や自己効力感が有意に上昇することを明らかにされていたが，長期的かつ広範囲のデータ収集・分析が必要であり，学習特性を可視化するために時間と手間を要した。本研究では，学習環境最適化のためのデータセット分析法を開発し，最小限のデータから短期間で学習特性を可視化することを可能とした。
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Report
(5 results)
Research Products
(36 results)
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[Journal Article] Development of an Analyzing System for Student’s Learning Characteristics by Visualization of Learning History2017
Author(s)
Morishita, T., Yokoyama, T., Niimura, M., Kunimune, H. and Higashibara, Y.
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Journal Title
Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education
Volume: -
Pages: 818-821
NAID
Related Report
Peer Reviewed
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[Presentation] Development of an Analyzing System for Student’s Learning Characteristics by Visualization of Learning History2017
Author(s)
Morishita, T., Yokoyama, T., Niimura, M., Kunimune, H. and Higashibara, Y.
Organizer
World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2017
Related Report
Int'l Joint Research
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