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   

This is the second webinar in a series designed for a broad audience of statistics and data science education researchers: young scholars beginning their research journeys, scholars from other disciplines interested in transitioning to statistics and data science education research, and existing statistics and data science researchers looking for new directions for their work.This session will focus on qualitative research: a family of "small n" research approaches that provide deep insights into human experience. An initial talk will provide an overview of qualitative research, followed by a discussant who will respond and add unique perspectives from the statistics education community. After the formal presentation and discussant contributions, there will be time for audience questions and related discussion.For reference, the prior webinars in this series are: What is Scholarship and Research in Statistics and Data Science Education? This webinar series is co-organized by CAUSE Research, the RSS Teaching Section, and Researchers of Statistics Education (RoSE) Network. Speaker Bio (Nicola Sochacka)Dr. Nicola (Nicki) Sochacka co-directs the ProQual Institute for Interpretive Research Methods, which builds capacity for high-quality qualitative and mixed methods research in STEM education. Drawing on the strengths STEM scholars bring to educational research—content expertise, teaching experience, and a drive for positive change—Nicki helps researchers design strategic, well-aligned studies through ProQual’s 9-week online course. This novel, person-centered program empowers participants to make informed decisions at every stage of the research process. Prior to founding the ProQual Institute, Nicki was Associate Director for Research Initiation at the Engineering Education Transformations Institute (EETI) at the University of Georgia. Supported by over $3.5M in NSF funding, her research explored empathy in engineering, the role of shame in identity development, arts-STEM integration, and innovative approaches to interpretive research. She is an experienced mentor, writing coach, and advisor who thrives on helping scholars design, publish, and propagate meaningful interpretive work. Discussant Bio (Kelly Findley)Kelly is a Teaching Associate Professor at the University of Illinois Urbana-Champaign. He earned a PhD in Curriculum and Instruction from Florida State University, focusing his dissertation on the disciplinary perspectives and experiences of graduate teaching assistants in statistics. Kelly primarily conducts qualitative research that seeks to capture rich examples of student thinking. He aims to create work that builds theory regarding both students' intuition about statistical concepts and students’ perspectives of the broader discipline.    

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

 PANELISTS:   Dr. Jaya Satagopan, Rutgers School of Public Health, Rutgers University (moderator) Dr. Christine Arcari, Celia Scott Weatherhead School of Public Health and Tropical Medicine, Tulane University Dr. Justin Post, Department of Statistics, North Carolina State University Dr. Juan (Jay) Klopper, Milken Institute School of Public Health, The George Washington UniversityDATE/TIME: Tuesday, February 25, 2025, 3:00pm Eastern Time (US & Canada)VENUE: Online webinar hosted using the Zoom platformABSTRACT: In this dynamic panel webinar, explore how artificial intelligence (AI) could revolutionize the way statistics is taught in health sciences. From automating data simulations and creating adaptive learning environments to leveraging generative AI tools for personalized education, this session discusses methods to enhance both teaching efficiency and student engagement. Discussion topics will include: utilizing AI to design interactive statistical visualizations, automating repetitive tasks like data cleaning in student projects, implementing generative AI to support self-paced learning, and addressing ethical considerations and biases in AI-driven education tools. This webinar is ideal for educators, researchers, and professionals in health sciences who are curious about integrating AI to enrich their teaching practices.REGISTRATION: To register for the webinar, please complete this form:  https://uniofqueensland.syd1.qualtrics.com/jfe/form/SV_77BxwrhuXvi8Z7gWe will send the Zoom link for the webinar to your email address the day before the webinar.