Join us for the next CAUSE webinar Tuesday, April 27th at 4:00PM ET.
Title: The use of algorithmic models to develop understanding of statistical modeling
Presenters: Andrew Zieffler (University of Minnesota) & Nicola Justice (Pacific
Lutheran University)
Date and Time: Tuesday, April 27, 2021
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. Andrew Zieffler is a Senior Lecturer and
researcher in the Quantitative Methods in Education program within the Department of
Educational Psychology at the University of Minnesota. His scholarship focuses on
statistics education. His research interests have recently focused on teacher education
and on how data science is transforming the statistics curriculum. You can read more about
his work and interests at
https://www.datadreaming.org/. Nicola Justice studies how
students and teachers learn statistics. As an assistant professor at Pacific Lutheran
University, her passion is to help students develop into skillful and ethical data
storytellers. When not teaching or learning, she likes to get outside with her family:
hiking, exploring, and throwing rocks in water.
Registration link:
https://psu.zoom.us/webinar/register/WN_sVBPlOp9QXWLi7SxwcBIMw