Optimizing assignment of students to courses based on learning activity analytics

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students' learning behavior patterns and teachers' approach to teaching. As a result, students may be less satisfied, have a lower level of understanding of the material, and achieve less. To solve these problems, we propose a strategy to optimize the assignment of students to courses based on learning activity analytics. The contributions of this study are 1) clarifying the relationship between learning behavior pattern and teaching based on learning activity analytics using large-scale educational data, 2) optimizing the assignment of students to courses based on learning behavior pattern analytics, and 3) demonstrating the effectiveness of assignment optimization via simulation experiments.

本文言語英語
ホスト出版物のタイトルEDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
編集者Collin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou
出版社International Educational Data Mining Society
ページ178-187
ページ数10
ISBN(電子版)9781733673600
出版ステータス出版済み - 1 1 2019
イベント12th International Conference on Educational Data Mining, EDM 2019 - Montreal, カナダ
継続期間: 7 2 20197 5 2019

出版物シリーズ

名前EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining

会議

会議12th International Conference on Educational Data Mining, EDM 2019
国/地域カナダ
CityMontreal
Period7/2/197/5/19

All Science Journal Classification (ASJC) codes

  • 情報システム
  • コンピュータ サイエンスの応用

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