| Project/Area Number |
17H01837
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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 | The University of Tokyo |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
松田 源立 成蹊大学, 理工学部, 准教授 (40433700)
山口 和紀 東京大学, 大学院総合文化研究科, 教授 (80158097)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥10,270,000 (Direct Cost: ¥7,900,000、Indirect Cost: ¥2,370,000)
Fiscal Year 2019: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2018: ¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | シラバス / ウェブクローラ / 機械学習 / カリキュラム / 計算機科学 / クローリング / ウェブクローリング / クローラ |
|---|
| Outline of Final Research Achievements |
This study aims to understand the characteristics and trends of the curriculum without any subjective elements through a quantitative analysis. In order to realize the goal, we obtained a large amount of syllabus information, and categorized them into the following three types: Link Type, which is a collection of links to syllabus web pages; Whole Type, which is a collection of multiple syllabus information; and Database Type, which gathers and provides syllabus information offered by educational institution. Then, we developed a syllabus collection support system which consists of a decision tool using a decision model by the SVM for each classification, a syllabus crawler that searches for pages that are strongly related to the syllabus based on keywords via the Google Search API and combines it with a complementary generic crawler, and a database which holds the web pages and their meta-information, and analysis results by the decision tool.
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| Academic Significance and Societal Importance of the Research Achievements |
高等教育機関のカリキュラムを客観的な基準で分析するためには、その内容を端的に表すシラバスを大量に集めて分析することが望ましい。しかし、その分析のためには多大な労力を要する。本研究では、シラバスの提供形態の分類と、それに合わせたシラバスの判定から得られた知見に基づく、シラバス収集支援システムを開発したことで、今後大量のシラバスを効率的に集める一助となる。
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