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2016 Fiscal Year Final Research Report

Development of SVM-Based IR System for Professional Development at Japanese Universities

Research Project

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Project/Area Number 26282057
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypePartial Multi-year Fund
Section一般
Research Field Educational technology
Research InstitutionTokyo Metropolitan University

Principal Investigator

Yamashita Hideaki  首都大学東京, 社会(科)学研究科, 教授 (30200687)

Co-Investigator(Kenkyū-buntansha) 立石 慎治  国立教育政策研究所, 生徒指導・進路指導研究センター, 研究員 (00598534)
大森 不二雄  首都大学東京, 大学教育センター, 教授 (10363540)
永井 正洋  首都大学東京, 大学教育センター, 教授 (40387478)
林 祐司  首都大学東京, 大学教育センター, 准教授 (40464523)
椿本 弥生  公立はこだて未来大学, システム情報科学部, 准教授 (40508397)
松河 秀哉  大阪大学, 全学教育推進機構, 助教 (50379111)
渡辺 雄貴  東京工業大学, 教育革新センター, 准教授 (50570090)
松田 岳士  首都大学東京, 大学教育センター, 教授 (90406835)
Co-Investigator(Renkei-kenkyūsha) TAKAMORI Tomotsugu  福島大学, 総合教育研究センター高等教育開発部門 (80583103)
Research Collaborator YANAGIURA Takeshi  Postsecondary Analytics, 代表
Project Period (FY) 2014-04-01 – 2017-03-31
Keywords教学IR / OR / 機械学習 / ラーニングアナリティクス / FD・SD
Outline of Final Research Achievements

In this study, the IR system that is useful for university faculty and staff in supporting students has been investigated. We developed an early alert system, or ‘Risk Detector’ (hereafter RD), to identify at-risk students before their potential risks become aware and to prevent the students from dropouts and holdovers. RD was expected to pinpoint high risk students with the machine learning method called Soft Margin Support Vector Machine and to indicate a list of metrics of each student as well as the prediction of dropout.
After feeding actual students’ data into the RD, its accuracy rate of prediction was 93%, which meant it provided highly reliable results of discriminant analysis and reasonable precision. In addition, RD was recognized trustworthy by faculty members who assessed it. On the contrary, poor UI and intelligibility of each value on RD were pointed out and discussed as future issues.

Free Research Field

オペレーションズ・リサーチ,経営工学

URL: 

Published: 2018-03-22  

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