Building an interpretable model of predicting student performance using comment data mining

Shaymaa E. Sorour, Tsunenori Mine

研究成果: 著書/レポートタイプへの貢献会議での発言

3 引用 (Scopus)

抄録

Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.

元の言語英語
ホスト出版物のタイトルProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
編集者Ayako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
出版者Institute of Electrical and Electronics Engineers Inc.
ページ285-291
ページ数7
ISBN(電子版)9781467389853
DOI
出版物ステータス出版済み - 8 31 2016
イベント5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, 日本
継続期間: 7 10 20167 14 2016

その他

その他5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
日本
Kumamoto
期間7/10/167/14/16

Fingerprint

Data mining
Students
Decision trees
Feedback

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

これを引用

Sorour, S. E., & Mine, T. (2016). Building an interpretable model of predicting student performance using comment data mining. : A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (版), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 285-291). [7557619] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.114

Building an interpretable model of predicting student performance using comment data mining. / Sorour, Shaymaa E.; Mine, Tsunenori.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 版 / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 285-291 7557619.

研究成果: 著書/レポートタイプへの貢献会議での発言

Sorour, SE & Mine, T 2016, Building an interpretable model of predicting student performance using comment data mining. : A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (版), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557619, Institute of Electrical and Electronics Engineers Inc., pp. 285-291, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, 日本, 7/10/16. https://doi.org/10.1109/IIAI-AAI.2016.114
Sorour SE, Mine T. Building an interpretable model of predicting student performance using comment data mining. : Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, 編集者, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 285-291. 7557619 https://doi.org/10.1109/IIAI-AAI.2016.114
Sorour, Shaymaa E. ; Mine, Tsunenori. / Building an interpretable model of predicting student performance using comment data mining. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編集者 / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 285-291
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