Reproducibility of findings from educational big data: A preliminary study

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Pages536-537
Number of pages2
ISBN (Electronic)9781450348706
DOIs
Publication statusPublished - Mar 13 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Other

Other7th International Conference on Learning Analytics and Knowledge, LAK 2017
CountryCanada
CityVancouver
Period3/13/173/17/17

Fingerprint

Teaching
Students
Big data

All Science Journal Classification (ASJC) codes

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

Cite this

Terai, M., Yamada, M., Okubo, F., Shimada, A., & Ogata, H. (2017). Reproducibility of findings from educational big data: A preliminary study. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 536-537). 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.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Terai, M, Yamada, M, Okubo, F, Shimada, A & Ogata, H 2017, Reproducibility of findings from educational big data: A preliminary study. in LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, pp. 536-537, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada, 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. In 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 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
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