| Project/Area Number |
18K11571
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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 62030:Learning support system-related
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| Research Institution | Yamaguchi University |
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Principal Investigator |
Murai Hiroshi 山口大学, 国際総合科学部, 准教授 (30279111)
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| Co-Investigator(Kenkyū-buntansha) |
Lee YongKyung 山口大学, 大学研究推進機構, 准教授 (70437698)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 学習支援 / e-Learning / 理解度判別 / 知財教育 / ニューラルネットワーク / 知識処理 / 学習支援システム / 知的財産教育 / ニューラルネット / 学習者支援 / パターン分類 / 知的財産権 |
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| Outline of Final Research Achievements |
In this study, we examine the design and implementation of an e-learning system to support intellectual-property (IP) education. The system consists of three functions: examination of online test using categorized questions, measurement of four academic skills of learners, and classification of learner’s understanding to recommend suitable learning contents for the learner. In addition, we implement a learning support system using classification of learners according to their type of learners' comprehension on IP education. The proposed system has an automatic recommendation function of learning contents. In order to support automatically, we use unsupervised classification method such as SOM. In order to support recommendation of suitable learning contents, we examine knowledge-based method for revising misclassification on learners' comprehension.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で取り組む，キーワードの抜け方に基づいた学習者の理解度タイプを判別する方法は，研究代表者らによる実践指導において把握されたものであり，さらに指導ノウハウをルール化した個別指導による学修支援は法律学習の分野では特に新規性が高い。提案手法を答案構成の構造解析に応用し，論述力育成を図る研究は，学術的独自性及び創造性を有するといえる。
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