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The use of algorithmic models to develop understanding of statistical modeling
Presented by:
Andrew Zieffler (University of Minnesota) & Nicola Justice (Pacific Lutheran University)

Abstract:
Classification trees and other algorithmic models are an increasingly important part of statistics and data science education. In the April CAUSE/Journal of Statistics and Data Science Education webinar series, we will talk with Andrew Zieffler and Nicola Justice, two of the co-authors of the forthcoming JSDSE paper entitled “The Use of Algorithmic Models to Develop Secondary Teachers' Understanding of the Statistical Modeling Process”: https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1900759

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 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.

Apr 27, 2021 04:00 PM in Eastern Time (US and Canada)

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