2020 Fiscal Year Final Research Report
Advanced learning assistance system with a large-scale collaborative database among universities
| Project/Area Number |
17H01842
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Learning support system
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| Research Institution | Kurume University (2020)
Hiroshima Institute of Technology (2017-2019) |
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Principal Investigator |
Hirose Hideo 久留米大学, 付置研究所, 客員教授 (60275401)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Keywords | 知的学習支援システム |
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| Outline of Final Research Achievements |
We have built an intelligent and reliable large-scale learning assisting system (online testing system) which can educate students having both problem-finding ability and problem-solving ability. The accumulated assessing results can be used as materials for formulating future education policies. For example, early detection of students at risk of dropout and alerting can prevent dropouts. In addition, learning analytics* suggests us the existence of evaluation bias by teachers when the description-type test is performed, and we have proposed a method to eliminate that bias.
*Learning Analytics: an attempt to analyze big data related to education and scientifically clarify how to give feedback to improve the educational field.
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| Free Research Field |
データサイエンス
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| Academic Significance and Societal Importance of the Research Achievements |
これまで、学習支援システムは教材の提供やカリキュラム支援を行うことが主体であったため、教育効果を定量的に評価することは困難であった。そこで、本研究では、現代テスト理論（IRT*）を積極的に取り入れることでテスト結果の評価を高い精度で行うことができることを示し、評価結果の見える化を促した。また、ラーニングアナリティクスによって将来の教育方針への指針を得ること可能性を示すことができた。システムには汎用性があるため、問題データベースなどの資源の大学間共有化が図れる。
*IRT：Item Response Theory, 項目反応理論ではテスト結果により受験者の能力値と問題の困難度を同時に求められる
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