| Project/Area Number |
15K04380
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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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| Research Field |
Sociology of education
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| Research Institution | Kaetsu University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
田島 悠史 宝塚大学, 東京メディア芸術学部, 特任講師 (20747729)
田尻 慎太郎 横浜商科大学, 商学部, 准教授 (90410167)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 中退分析 / IR / 機械学習 / 大学中退 / 教育社会学 |
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| Outline of Final Research Achievements |
The first of the results of this study was the improvement of data and the presentation of model creation conditions for expressing dropouts. The second is the creation of a student model, the teaching model, the third is the linking with the teaching service, and the fourth is the holding of seminars and workshops. In the first, three types of dropouts were suggested using interviews with dropout students and faculty members and academic data, and conditions for expressing dropouts were presented. In the second, the variables related to dropouts were organized into two types, macro variables related to the entire university and micro variables related to individual students, and a model was created to predict dropouts for each term, and its validity was verified. In the third, we proposed that it could be used in educational services using the created model. In the fourth, we widely published the findings obtained in this study at seminars etc.
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| Academic Significance and Societal Importance of the Research Achievements |
大学における中退状況は各大学によって大きく異なるが、中退をすることは学生にとっても、大学にとっても大きな負の影響を与えることになる。中退を防止するための施策はこれまで教職員の直感や勘によってなされることが多かった。本研究では大学に蓄積されている教学データを用いて、客観的な評価を教学サービスに適応することを試みた。教学データと学生データを用いて中退を学期ごとに予測するモデルを作成し、教学サービスと連携させることで、個々の学生にとってふさわしい防止施策を提示することができた。
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