The Use of Algorithmic Models to Develop Secondary Teachers' Understanding of the Statistical Modeling Process. Issue 1 (16th April 2021)

• Record Type: Journal Article
• Authors: Zieffler, Andrew; Justice, Nicola; delMas, Robert; Huberty, Michael D.
• Is Part Of: Journal of Statistics and Data Science Education. Volume 29:Issue 1(2021)
• Page Start: 131
• Page End: 147
• Publication Date: 2021-04-16
• Subjects: Algorithmic models -- Classification trees -- Data science education -- Statistics education research -- Statistical modeling -- Teacher development
• DOI: Https://www.tandfonline.com/doi/10.1080/26939169.2021.1900759
• Languages: English
• ISSNs: 2693-9169

Abstract

Abstract: Statistical modeling continues to gain prominence in the secondary curriculum, and recent recommendations to emphasize data science and computational thinking may soon position algorithmic models into the school curriculum. Many teachers' preparation for and experiences teaching statistical modeling have focused on probabilistic models. Subsequently, much of the research literature related to the teachers' understanding has focused on probabilistic models. This study explores the extent to which secondary statistics teachers appear to understand ideas of statistical modeling, specifically the processes of model building and evaluation, when introduced using classification trees, a type of algorithmic model. Results of this study suggest that while teachers were able to read and build classification tree models, they experienced more difficulty when evaluating models. Further research could continue to explore possible learning trajectories, technology tools, and pedagogical approaches for using classification trees to introduce ideas of statistical modeling.