Webinars

Abstract: In this March edition of the JSDSE/CAUSE webinar series, we highlight the recent article "Integrating Statistical Writing in an Applied Regression Course Using Small-Scale Writing Projects". Effective communication skills, both written and oral, are considered core skills for statisticians. This article presents five small-scale writing projects that were developed for an applied regression course, including the specific writing skills emphasized in each project and what each project entails. The authors also present and discuss results from surveys on changes in writing attitudes throughout the course and student feedback on the projects. The results indicate improved attitudes toward writing and a positive experience for students. Recommendations for incorporating the writing projects based on their observations of implementing them and potential changes are also provided. Read more in the JSDSE article: https://www.tandfonline.com/doi/full/10.1080/26939169.2025.2526626

Abstract:  In this January edition of the JSDSE/CAUSE webinar series, we highlight the recent article "Creative Conflict: Reacting to the Past Roleplaying Games in the Introductory Statistics Classroom". Reacting to the Past is a game-based pedagogy in which students take on roles in a broader historical conflict with elements of game fiction, competition, and collaboration. The article introduces two Reacting to the Past games developed for introductory statistics courses: “The Cigarette Century”: Tobacco and Lung Cancer, 1964-1965 (Cigarette Century) and Cholera! at the Pump: Contagionism, Miasma Theory and Sanitation, London 1854 (Cholera 1854). Both games are used to teach specific statistical content including measures of risk, data visualization, and hypothesis testing while also using historical context and real datasets to emphasize statistical thinking and provide relevance. Reacting to the Past games as high impact practices are strongly in alignment with the 2016 GAISE recommendations including conceptual understanding, use of real data, and active learning.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2025.2572340

 In this November edition of the JSDSE/CAUSE webinar series, we highlight the recent article Estimating Tanks - Communication in Mathematical Statistics. Communication skills are critical for statisticians, but in our curricula emphasis on communication tends to be in applied courses. After providing historical context around the traditional mathematical statistics course, this work introduces a project for the course allowing students to engage in writing about theoretical results. This project is one of two used in the course with writing components, and was introduced in the Spring of 2015 and revised for Spring of 2024. The project combines theoretical derivations, computation (via a simulation study) and practice with written communication skills into a single assignment. It was based on the historical German tank problem, estimating the number of tanks, N, produced based on assuming serial numbers on tanks were labeled 1 to N, and sampling a set of k tanks found in the field. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2025.2550996

In this September edition of the JSDSE/CAUSE webinar series, we highlight the recent article Developing Students’ Statistical Expertise Through Writing in the Age of AI. As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In their work, the authors engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Their results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist?  Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2025.2497547 

In this June edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article The Design and Implementation of a Bayesian Data Analysis Lesson for Pre-Service Mathematics and Science Teachers. With the rise of the popularity of Bayesian methods and accessible computer software, teaching and learning about Bayesian methods are expanding. However, most educational opportunities are geared toward statistics and data science students and are less available in the broader STEM fields. In addition, there are fewer opportunities at the K-12 level. With the indirect aim of introducing Bayesian methods at the K-12 level, the authors have developed a Bayesian data analysis activity and implemented it with 35 mathematics and science pre-service teachers. In their work, they describe the activity, the web app supporting the activity, and pre-service teachers’ perceptions of the activity. Lastly, they discuss future directions for preparing K-12 teachers in teaching and learning about Bayesian methods.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2362148   

In this May edition of the JSDSE/CAUSE webinar series, we highlight the recent article Why Swipe Right? Career Interests and Aspirations of Incoming Statistics Majors. Undergraduate statistics programs can help students hone a wide range of quantitative, computational, and communicative skills as they prepare for a fruitful career. In this talk, the authors explore what motivates students to choose a major in statistics and to what extent incoming statistics majors recognize these wider skills as part of doing statistics. To build theories regarding what motivates students toward statistics, they interviewed nine first-year statistics majors at a large public university and analyzed their responses using a grounded theory approach. Each student shared their views of “who” statistics is to them, what kind of career they aspired to, and what prior experiences oriented them toward studying statistics. A strong, cross-cutting theme that emerged was that of balance. For example, statistics appeared as a safe and lucrative career choice that catered to their mathematical strengths, but it could also be an exciting career choice that stoked their imaginations. The authors also found that students had different perspectives and expectations about the role of mathematics and coding that may impact their experience in the major. Implications for introductory course curricula, the importance of projects, and outreach programs are discussed.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2430244

In this April edition of the JSDSE/CAUSE webinar series, we highlight the recent article A New Era of Learning: Considerations for ChatGPT as a Tool to Enhance Statistics and Data Science Education. ChatGPT is one of many generative artificial intelligence (AI) tools that has emerged recently, creating controversy in the education community with concerns about its potential to be used for plagiarism and to undermine students’ ability to think independently. This talk focuses on the potential of ChatGPT as an educational tool for statistics and data science. It encourages readers to consider the history of trepidation surrounding introducing new technology in the classroom, such as the calculator. The presenters explore the possibility of leveraging ChatGPT’s capabilities in statistics and data science education, providing examples of how ChatGPT can aid in developing course materials and suggestions for how educators can prompt students to interact with ChatGPT responsibly.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2223609 

In this March edition of the JSDSE/CAUSE webinar series, we highlight the recent article Studying the Opportunities Provided by an Applied High School Mathematics Course: Explorations in Data Science. The authors report on a multi-method study of a high school course in data science, finding that students who take data science take more mathematics courses than those who do not, there are more under-represented students in data science than is typical for other advanced mathematics courses; that the students who take data science are more positive about a future in STEM and they tend to be older. Analysis of writing from the students shows that students are very positive about the course, appreciating the relevance of the content, the opportunity to investigate ideas, the chance to learn challenging, applied content, and the opportunity to think creatively. In an assessment of data and functions given to students in data science and Algebra 2 courses, the students in data science scored at significantly higher levels.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2333735

In this February edition of the JSDSE/CAUSE webinar series, we highlight the recent article Generative AI for Data Science 101: Coding Without Learning To Code. Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, the authors saw an opportunity for a middle ground, which they tried in Fall 2023 in a required introductory data science course in their school’s full-time MBA program. In this webinar, the authors share their experience teaching students how to write English prompts to the artificial intelligence tool GitHub Copilot that could be turned into R code and executed.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2432397  

In this January edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Red Light Reaction – A Statistics Project with Real Life Application. The presenters describe a hands-on project in which students collect data on the impact of distracted driving on driver reaction time. Initially they do this in class via a virtual driving applet, using themselves and fellow students as test subjects. Different applet versions simulate driving with and without distraction and measure the time it takes to apply brakes after the red brake lights of the car ahead appear. Students use the collected data to practice a range of statistical techniques, such as assessing data for outliers, creating a hypothesis to be tested, performing an appropriate test, and then translating their results to determine a safe driving distance. In the second part of the project, students work in groups outside of class. Each group recruits a category of test subjects (e.g., athletes, video gamers, STEM majors) of their choosing, collects data, and performs statistical analysis. Finally, students develop hypotheses as to whether different categories of drivers have better or worse reaction times, collect additional relevant data, and perform the appropriate statistical test.Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2407781