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  • 标题:A Study on Student Performance, Engagement, and Experience With Kaggle InClass data Challenges
  • 本地全文:下载
  • 作者:Julia Polak ; Dianne Cook
  • 期刊名称:Journal of Statistics Education
  • 电子版ISSN:1069-1898
  • 出版年度:2021
  • 卷号:29
  • 期号:1
  • 页码:63-70
  • DOI:10.1080/10691898.2021.1892554
  • 出版社:American Statistical Association
  • 摘要:Kaggle is a data modeling competition service, where participants compete to build a model with lower predictive error than other participants. Several years ago they released a simplified service that is ideal for instructors to run competitions in a classroom setting. This article describes the results of an experiment to determine if participating in a predictive modeling competition enhances learning. The evidence suggests it does. In addition, students were surveyed to examine if the competition improved engagement and interest in the class. Supplementary materials for this article are available online.
  • 关键词:Data mining ; Data science ; Instructional technology ; Statistical modeling ; Statistics education
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