ERIC Number: EJ1352086
Record Type: Journal
Publication Date: 2021
Pages: 8
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2693-9169
Available Date: N/A
A Study on Student Performance, Engagement, and Experience with Kaggle InClass Data Challenges
Polak, Julia; Cook, Dianne
Journal of Statistics and Data Science Education, v29 n1 p63-70 2021
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.
Descriptors: Artificial Intelligence, Data Analysis, Models, Competition, Prediction, Educational Technology, Statistics Education, College Students, Foreign Countries, Automation, Performance, Outcomes of Education, Learner Engagement
Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research; Tests/Questionnaires
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Australia
Grant or Contract Numbers: N/A
Author Affiliations: N/A