2017年3月13日
Reproducibility of findings from educational big data: A preliminary study
ACM International Conference Proceeding Series
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- 開始ページ
- 536
- 終了ページ
- 537
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1145/3027385.3029445
- 出版者・発行元
- Association for Computing Machinery
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.
- リンク情報
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- DOI
- https://doi.org/10.1145/3027385.3029445
- DBLP
- https://dblp.uni-trier.de/rec/conf/lak/OiYOSO17
- URL
- http://dl.acm.org/citation.cfm?id=3029445
- URL
- http://dblp.uni-trier.de/db/conf/lak/lak2017.html#conf/lak/OiYOSO17
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85016477103&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85016477103&origin=inward
- ID情報
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- DOI : 10.1145/3027385.3029445
- DBLP ID : conf/lak/OiYOSO17
- SCOPUS ID : 85016477103