Reproducibility of findings from educational big data

A preliminary study

Misato Terai, Masanori Yamada, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

研究成果: 著書/レポートタイプへの貢献会議での発言

2 引用 (Scopus)

抄録

In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.

元の言語英語
ホスト出版物のタイトルLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
ホスト出版物のサブタイトルUnderstanding, Informing and Improving Learning with Data
出版者Association for Computing Machinery
ページ536-537
ページ数2
ISBN(電子版)9781450348706
DOI
出版物ステータス出版済み - 3 13 2017
イベント7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, カナダ
継続期間: 3 13 20173 17 2017

出版物シリーズ

名前ACM International Conference Proceeding Series

その他

その他7th International Conference on Learning Analytics and Knowledge, LAK 2017
カナダ
Vancouver
期間3/13/173/17/17

Fingerprint

Teaching
Students
Big data

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

これを引用

Terai, M., Yamada, M., Okubo, F., Shimada, A., & Ogata, H. (2017). Reproducibility of findings from educational big data: A preliminary study. : LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 536-537). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029445

Reproducibility of findings from educational big data : A preliminary study. / Terai, Misato; Yamada, Masanori; Okubo, Fumiya; Shimada, Atsushi; Ogata, Hiroaki.

LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. p. 536-537 (ACM International Conference Proceeding Series).

研究成果: 著書/レポートタイプへの貢献会議での発言

Terai, M, Yamada, M, Okubo, F, Shimada, A & Ogata, H 2017, Reproducibility of findings from educational big data: A preliminary study. : LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 536-537, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, カナダ, 3/13/17. https://doi.org/10.1145/3027385.3029445
Terai M, Yamada M, Okubo F, Shimada A, Ogata H. Reproducibility of findings from educational big data: A preliminary study. : LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery. 2017. p. 536-537. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3027385.3029445
Terai, Misato ; Yamada, Masanori ; Okubo, Fumiya ; Shimada, Atsushi ; Ogata, Hiroaki. / Reproducibility of findings from educational big data : A preliminary study. LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. pp. 536-537 (ACM International Conference Proceeding Series).
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