2018 Fiscal Year Final Research Report
Estimation of Latent Skill and Visualization of Skill Structure by Educational Data Mining
| Project/Area Number |
16K01095
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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 |
Educational technology
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| Research Institution | Kisarazu National College of Technology |
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Principal Investigator |
Oeda Shinichi 木更津工業高等専門学校, 情報工学科, 准教授 (80390417)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | 教育データマイニング / 潜在的スキル構造 / Q-matrix / プログラミング教育 / NMF / Skill Modeling / Student Modeling |
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| Outline of Final Research Achievements |
Intelligent tutoring systems (ITSs) and learning management systems (LMSs) have been widely used in the field of education. They allow the collection of log-data from learners, and especially, students. In this research, we developed the method of educational data mining that extracts latent skill to acquire knowledge from data, such as ITSs and LMSs.
We have developed the skill modeling which extracts the relationship questions and skills from examination results. we also have developed the novel educational data mining method using machine learning that discovers the rule and knowledge from log-data during class.
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| Free Research Field |
知能情報学，数理情報学，教育工学
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| Academic Significance and Societal Importance of the Research Achievements |
国内ではIT技術者の不足が深刻になっている．さらにスマートフォンの普及やSNS利用者の増加，IoT分野の成長，新たなビッグデータ解析手法の開発，人工知能(AI)の利活用に向けて，これからさらに多くのIT技術者が必要とされることは想像に難くない．
そこで本研究の目的は，調査対象をプログラミング教育とし，教育データからスキル修得過程を解明することとした．特に，本研究の学術的独自性は，特殊な装置や教育方法の変更を必要とせずに，教育現場から得られるありのままのデータに対し，数理に基づいたデータサイエンス技術を適用して潜在的スキルの形成過程を解明する点である．
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