Webinars

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.

In the March CAUSE/Journal of Statistics and Data Science Education webinar series we will discuss two related papers on data ingestation, data collection, and data analysis.   Mine Dogucu (UC Irvine) will discuss her paper "Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities" (https://github.com/mdogucu/web-scrape). Albert Y. Kim (Smith College) will discuss his paper "'Playing the Whole Game': A Data Collection and Analysis Exercise With Google Calendar" (https://smithcollege-sds.github.io/sds-www/JSE_calendar.html) Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. Her work focuses on modern pedagogical approaches in the statistics curriculum, making data science education accessible, and undergraduate Bayesian education. She is the coauthor of the upcoming book Bayes Rules! An Introduction to Bayesian Modeling with R. She co-chairs the Undergraduate Statistics Project Competition and the Electronic Undergraduate Statistics Research Conference (USPROC+eUSR). She shares her thoughts about data science education on her Data Pedagogy blog. Albert Kim is an Assistant Professor of Statistical & Data Sciences at Smith College as well as a Visiting Scholar at the ForestGEO network's Smithsonian Conservation Biology Institute (SCBI) large forest dynamics plot. His research centers on forest ecology, in particular modeling the impact of climate change on the growth of trees as well as ecological forecasting. He is a co-author of "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" (see moderndive.com).

The Journal of Statistics and Data Science Education recently published a cluster of papers on Bayesian methods (https://www.tandfonline.com/toc/ujse20/28/3?nav=tocList). The Bayesian cluster includes a presentation of how and why Bayesian ideas should be added to the curriculum; guidance on how to structure a semester-long Bayesian course for undergraduates; a discussion of the ever-evolving nature of Bayesian computing; a book review; and a panel interview of several Bayesian educators. For the February CAUSE/JSDSE webinar series, we’ve invited several authors of these provocative and informative articles to describe their work and its implications for statistics and data science education. Jingchen (Monika) Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar, which is shared online across several liberal arts colleges. Her research focuses on dealing with data privacy issues by releasing synthetic data. Kevin Ross is an Associate Professor of Statistics at Cal Poly San Luis Obispo. His research interests include probability, stochastic processes and applications as well as probability and statistics education. Colin Rundel is a lecturer at the University of Edinburgh and an assistant professor of the practice in Statistical Science at Duke University. His research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods.

The Journal of Statistics and Data Science Education special issue on “Computing in the Statistics and Data Science Curriculum” features a set of papers that provide a mosaic of curricular innovations and approaches that embrace computing. The collected papers (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. In this webinar, we've invited two authors of papers in the special issue to talk about their work and to answer questions originally posed by Nolan and Temple Lang in their 2010 TAS paper "Computing in the Statistics Curriculum": When they graduate, what ought our students be able to do computationally, and are we preparing them adequately in this regard? Do we provide students the essential skills needed to engage in statistical problem solving and keep abreast of new technologies as they evolve? Do our students build the confidence needed to overcome computational challenges to, for example, reliably design and run a synthetic experiment or carry out a comprehensive data analysis? Overall, are we doing a good job preparing students who are ready to engage in and succeed at statistical inquiry?

Statistics courses that focus on data analysis in isolation, discounting the scientific inquiry process, may not motivate students to learn the subject. By involving students in other steps of the inquiry process, such as generating hypotheses and data, students may become more interested and vested in the analysis step. Additionally, such an approach might better prepare students to tackle real research questions outside of the statistics classroom. Presented here is a classroom activity utilizing the popular Hasbro board game Operation, which requires student involvement in the entire research process. Highlighted are ways this activity uncovers a number of research issues. A number of categorical and continuous variables are collected, making the activity amenable to a variety of statistical investigations and thus easy to imbed into any curriculum. Designed to mimic a real-world research scenario, this fun activity provides a guided yet flexible research experience from start to finish.

Based on our March 2015 JSE paper "Identifying Statistical Concepts Associated with High and Low Levels of Self-Efficacy to Teach Statistics in Middle Grades,” we discuss the results of a Rasch modeling analysis of pre-service mathematics teacher responses to the middle grades Self-Efficacy to Teach Statistics (SETS) instrument. We share how we used Rasch measurement theory to develop the middle grades SETS instrument to measure pre-service teachers’ self-efficacy to teach topics at GAISE levels A and B as well as K–8 CCSSM statistics topics. SETS items ask teachers to rate their self-efficacy to teach a particular concept on a Likert scale from 1 (“not confident at all”) to 6 (“completely confident”). From data collected at four public institutions of higher education in the United States, we discuss what statistics topics pre-service teachers felt the most (or least) efficacious about and how that informs our continuing work.

We describe the assistive technologies used to accommodate a blind student who took a second course in statistics at Wake Forest University. The course covered simple and multiple regression, model diagnostics, model selection, data visualization, and elementary logistic regression. These topics required that the student both interpret and produce three sets of materials: mathematical writing, computer programming, and visual displays of data. We relied heavily on integrating the use of multiple existing technologies. Specifically, this talk will detail the extensive use of screen readers, LaTeX, a modified use of R and the BrailleR package, a desktop Braille embosser, and a modified classroom approach.

In this presentation, we will compare three delivery methods of an introductory statistical literacy course, all taught by the same instructor in the same semester for over 400 students. The complications of defining specific delivery methods and the pros and cons of choices of assessments will also be discussed.

In this webinar, we will present lecture material and activities that introduce metrology, the science of measurement, which were developed and tested in a pilot study at Iowa State University. Our motivation for the newly developed material stems from the observation that many undergraduate students who have just completed an introductory statistics course still lack a deeper understanding of variability and enthusiasm for the field of statistics. The materials explain how to characterize sources of variability in a dataset, in a way that is natural and accessible, because the sources of variability are observable. Everyday examples of measurements, such as the amount of gasoline pumped into a car, are presented, and the consequences of variability within those measurements are discussed. A corresponding article in the November issue of Journal of Statistics Education shows most students who were exposed to the material improved their understanding of variability and had a greater appreciation of the value of statistics.

We motivate and illustrate a lesser-known dynamic physical model for the median, offer pedagogical discussion and support, and share results of a pilot assessment with pre-service middle school teachers. Before the webinar, we invite you to browse our article "http://www.amstat.org/publications/jse/v22n3/lesser.pdf" , or at least watch the 1-minute video http://www.amstat.org/publications/jse/v22n3/pulley_loop_physical_model_of_median.html of the model in action.