Literaturnachweis - Detailanzeige
| Autor/inn/en | Polak, Julia; Cook, Dianne |
|---|---|
| Titel | A Study on Student Performance, Engagement, and Experience with Kaggle InClass Data Challenges |
| Quelle | In: Journal of Statistics and Data Science Education, 29 (2021) 1, S.63-70 (8 Seiten)
PDF als Volltext |
| Zusatzinformation | ORCID (Cook, Dianne) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| DOI | 10.1080/10691898.2021.1892554 |
| Schlagwörter | Artificial Intelligence; Data Analysis; Models; Competition; Prediction; Educational Technology; Statistics Education; College Students; Foreign Countries; Automation; Performance; Outcomes of Education; Learner Engagement; Australia |
| 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. (As Provided). |
| Anmerkungen | 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 |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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