A Study on Student Performance, Engagement, and Experience With Kaggle InClass data Challenges. Issue 1 (4th April 2021)

• Record Type: Journal Article
• Authors: Polak, Julia; Cook, Dianne
• Is Part Of: Journal of Statistics and Data Science Education. Volume 29:Issue 1(2021)
• Page Start: 63
• Page End: 70
• Publication Date: 2021-04-04
• Subjects: Data mining -- Data science -- Instructional technology -- Statistical modeling -- Statistics education
• DOI: Https://www.tandfonline.com/doi/10.1080/10691898.2021.1892554
• Languages: English
• ISSNs: 2693-9169

Abstract

Abstract: 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.