Webinars

 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.  

This talk is 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. The talk will begin with a description of the types of research areas and questions of interest to the field of statistics and data education research. As time allows, we will consider how to move from interesting ideas to researchable questions and how to connect ideas to existing literature to maximise the potential of your research to move the field of statistics and data science education research forward. After the formal presentation and discussant contributions, there will be time for audience questions and related discussion. Speaker bio: Dr Jennifer J Kaplan has been an active researcher in statistics and data science education since the early 2000s. She served as the Editor of Regular Papers for the Statistics Education Research Journal (SERJ) from 2018 – 2023 and has been an Associate Editor for the Journal of Statistics and Data Science Education (JSDSE) since 2010. She is probably best known for her research in lexical ambiguity, studying the effects of the use of statistical terms that have competing meanings in everyday usage. In general, her research program combines learning in undergraduate statistics courses in HE with professional development of instructors of undergraduate students. Dr Kaplan is currently the Director of the PhD Program in Mathematics and Science Education (MSE) at Middle Tennessee State University (MTSU) and lives within biking distance of the geographic center of the state of Tennessee. Discussant bio: Professor Rhys C Jones (Professor of Statistical Literacy) is an internationally recognised educational leader with extensive experience in curriculum development and theory, statistics education, and engaging students in small and large classroom settings (offline and online). Rhys is an International Statistical Institute Elected member, a member of both the Teaching Statistics Section and the Education Policy Advisory Group of the Royal Statistical Society, and a trustee of the Teaching Statistics Trust. He has held lecturing positions at Cardiff University, London Southeast College, and Birmingham City University. Over his career, he has taught in a variety of fields, at undergraduate and postgraduate levels, including statistics, quantitative methods, mathematics for science, teacher training, research methods, biomedical science, nutrition and organic chemistry, health and well-being, and clinical anatomy and physiology. His primary research contributions are in the areas of curriculum development, randomness misconceptions, and the role of context in statistics education.  Registration is currently open up to the time of the event.Chair and contact: Dr Margaret MacDougall (email: margaret.macdougall@ed.ac.uk)

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  

This CAUSE webinar will feature a panel of experienced mentors who have successfully guided students in the Undergraduate Statistics Project Competition (USPROC). The panelists will share insights from their experiences mentoring award-winning student projects and discuss strategies for fostering student success in both classroom statistics projects and independent research. Attendees will receive practical advice on how to effectively mentor undergraduate students in research, including project development, data analysis, and communication of results, while inspiring a passion for statistics and research excellence.

In this December edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data. Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics teachers to learn new statistical and computational approaches such as digital image analysis and classification models. Using their design framework, the authors describe construction of a task that introduces classification modeling using grayscale digital images. The task was implemented within a teaching experiment involving six high school statistics teachers. Their findings from this exploratory study indicated that the task design seemed to support statistical and computational thinking practices related to classification modeling and digital image data. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2351570  

In this November edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Simulation-Based Inference: Random Sampling vs. Random Assignment? What Instructors Should Know. “Simulation-based inference” is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this webinar, the presenters highlight for teachers some implications of different simulation strategies when analyzing two variables. In particular, does it matter whether the simulation models random sampling or random assignment? They present examples from comparing two means and simple linear regression, highlighting the impact on the standard deviation of the null distribution. They also highlight some possible extensions that simulation-based inference easily allows. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2333736  

TSHS Fall 2024 Webinar: “Clarifying practical relevance of p-values and confidence intervals. Clear choice of words and images, that foster conceptual understanding and steer away from misconceptions.” The ASA Section on Teaching of Statistics in the Health Sciences (TSHS) is excited to present our Fall 2024 webinar. Our speaker will be Hilary Watt of the Imperial College London. She will discuss the practical relevance of p-values and confidence intervals. The webinar is FREE and open to all. Details and registration information are below. TITLE: Clarifying practical relevance of p-values and confidence intervals. Clear choice of words and images, that foster conceptual understanding and steer away from misconceptions. PRESENTER: Hilary Watt, Imperial College London DATE/TIME: Tuesday, October 29, 2024, 3:00pm Eastern Time (US & Canada) VENUE: Online webinar hosted using the Zoom platform ABSTRACT: Informal p-value and CI interpretations are often used in health science teaching yet may be too crude to help much in developing understanding. Not all students deduce CIs and p-values practical relevance from their definitions. This talk suggests transparent explanatory interpretations, that promote understanding of their practical relevance. They are compact enough for routine repetition whilst teaching. Referring to the sample (not blood sample!) as participants provides a clear starting point. Repeatedly clarifying the relationship of participants to the population, including the random sampling assumption, addresses the issue that ‘population’ is poorly understood. Implications of random sampling being impractical can be discussed. Images of the distribution of sample statistics are useful for understanding calculation methods yet can readily be misinterpreted. Strategies are suggested to offset the risk of common misinterpretations. This includes use of images designed to foster conceptual understanding, for both CIs and for p-values (for p-values, illustrating their continuous nature). The intention is to make statistics easier and more intuitive to understand. The inspiration is to steer towards higher standards of data interpretation within health sciences. REGISTRATION: To register for the webinar, please complete this form: https://uniofqueensland.syd1.qualtrics.com/jfe/form/SV_4I9TBkB3xKNS7AO We will send the Zoom link for the webinar to your email address the day before the webinar.

In this October edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Philosophy within Data Science Ethics Courses. There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, the speakers present a framework for bringing together key data science practices with ethics topics. The ethics topics were collated from sixteen data science ethics courses with public-facing syllabi and reading lists. The speakers encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which are rife with potentially morally charged decision points. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2394542   

In this September edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article What They Remember May Not Be What They Understand: A Study of Mnemonic Recall and Performance by Introductory Statistics Students. The speakers will discuss a large-scale (n = 1323) survey of mnemonic recall, self-reported familiarity, cued explanation, and application by introductory statistics students that was conducted at a large research university in the southeastern United States. The students were presented 14 mnemonics during the fall 2017 term. Different nonoverlapping cohorts of students were asked at different time points to complete a survey about mnemonic use. At each time point, the students were asked to recall any mnemonic that they remembered, explain the mnemonic when cued, self-report their degree of familiarity, and apply the mnemonic. Of the 14 mnemonics, acronym-type mnemonics were recalled more frequently, but longer phrase-type mnemonics were explained and applied more often. These findings suggest that instructors should provide scaffolding to move a student from recalling a mnemonic to using a mnemonic toward successful completion of the statistics problem at hand. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2334905