@article{3d00267e1ce84ba19e9f2d38441fb373,
title = "A study on student performance, engagement, and experience with Kaggle InClass data challenges",
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.",
keywords = "Data mining, Data science, Instructional technology, Statistical modeling, Statistics education",
author = "Julia Polak and Dianne Cook",
note = "Funding Information: This project (title: Effect of Data Competition on Learning Experience) has been approved by the Faculty of Science Human Ethics Advisory Group University of Melbourne (ID: 1749858.1 on September 4, 2017) and by Monash University Human Research Ethics Committee (ID: 9985 on August 24, 2017). This document was produced in R (R Core Team 2017) with the package knitr (Xie 2015). Data cleaning was conducted using tidyr (Wickham and Henry 2018), dplyr (Wickham et\u00A0al. 2017) and plots were made with ggplot2 (Wickham 2016). The materials to reproduce the work are available at https://github.com/dicook/paper-quoll. Publisher Copyright: {\textcopyright} 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.",
year = "2021",
doi = "10.1080/10691898.2021.1892554",
language = "English",
volume = "29",
pages = "63--70",
journal = "Journal of Statistics and Data Science Education",
issn = "2693-9169",
publisher = "Taylor & Francis",
number = "1",
}