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
CAUSE will soon launch a collection of SPARKS - very short (only 10-20 seconds) video clips that could be used in teaching statistics, by depicting an interesting real-world context and either explicitly posing a question or implicitly positioning a teacher/student to pose many questions to investigate. Or maybe you have a really short poem, joke, or jingle! To give you an incentive to participate in building this collection, submissions before USCOTS 2021 will earn you complimentary registration!
The submission page is at https://www.causeweb.org/cause/sparks/submit<https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cause…> and includes several example videos. Selected videos from the collection will be shown at USCOTS (which starts on June 28).
The Consortium for the Advancement of Undergraduate Statistics Education is happy to announce our 59th Cartoon Caption Contest – now ongoing every month for nearly five years! Each month a cartoon, drawn by British cartoonist John Landers, is posted for you and your students to suggest statistical captions (cartoons are posted at the beginning of the month and submissions are due at the end of the month). The caption contest is offered as a fun way to get your students thinking independently about statistical concepts.
The next cartoon and the entry rules for the contest ending April 30 are at
The best captions will be posted on CAUSEweb and the winner(s) will receive their choice of a coffee mug or t-shirt imprinted with the cartoon and their caption.
March Results: The March caption contest featured a cartoon showing a hospital room but instead of patients being examined by medical staff, the medical staff are being examined by regular people. The winning caption for the March contest was “Cross-over design … gone wrong,” written by Kelly Spoon from San Diego Mesa College. Kelly’s caption can be used to introduce the value of cross-over designs for reducing variability. Honorable mentions this month go to Louis Rocconi from University of Tennessee for the caption: “Would you trust your medical care to just anyone? Then don't trust your statistical analysis to just anyone either!” and to George Divine from Henry Ford Hospital for his caption “Some flipped classroom situations may work better than others!” and also to Sara Letardi from Istat for her contribution focusing on conditional probability: “Is it more likely that a doctor is also a patient or that a patient is also a doctor? Let's ask Mr. Bayes!”
Thanks to everyone who submitted a caption and congratulations to our winners!