Webinars

While computing and data shape nearly every aspect of modern life, efforts to expand data and computing education in K-12 settings have grown rapidly but unevenly. The 2026 National Academies “Developing Competencies for the Future of Data and Computing: The Role of K-12” consensus report (LINK: https://www.nationalacademies.org/projects/DBASSE-BOSE-23-04/) identified and described seven competencies that are critical for students to thrive in a data-driven computational world: 1) problem posing and problem-solving processes; 2) producing and working with data; 3) abstraction, algorithmic thinking, and automation; 4) probabilistic and inferential reasoning; 5) models and representations; 6) technology and society; and 7) data and computing systems. The report suggests a road map to use these competencies to improve the integration of data and computing into primary and secondary education.What are the implications of the growth of K-12 computer science, data science, machine learning, and artificial intelligence and the seven competencies on undergraduate statistics education? How does the report connect to the GAISE (Guidelines for Assessment and Instruction in Statistics Education) K-12 and GAISE College reports? How can we build on the proposed integrated framework in productive ways? In this webinar, four members of the consensus report committee will consider these critical questions, provide an overview of the consensus report, and offer prognostications about the future of undergraduate statistics education.  

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.

Every five years since 1965, on behalf of the Conference Board of the Mathematical Sciences (CBMS), the American Mathematical Society (AMS) has conducted a national survey of undergraduate mathematics and statistics programs and published reports detailing characteristics of curricula, course delivery, enrollments, instructional staff, student outcomes, and more. The planned-for 2020 Survey has turned out to be a departure from the past, taking place in two parts—late last year in a mid-pandemic survey focused on departments’ experiences with the effects of COVID-19, and then later in 2021 as a continuation of the larger longitudinal study begun decades ago. The panelists will discuss the objectives of the study, relate a few data stories emanating from prior iterations, share some of the COVID survey findings, and provide a look forward to the upcoming Survey and its follow-up.

Statistics is more important than ever in today's data-driven world. This is reflected in the increased level of statistics understanding expected of K-12 students according to the CCSS-M and state-level standards. To develop middle and high school teachers' statistical knowledge for teaching, the MODULE(S^2) project has created curriculum materials for use in introductory statistics course(s) that preservice secondary teachers take. The materials develop preservice teachers’  subject matter and pedagogical content knowledge for teaching statistics as well as their equity literacy. In this webinar, we will provide an introduction to these materials including examples of statistical tasks and classroom videos from the materials. Alignment of these materials with ASA’s GAISE, ASA’s Statistical Education of Teachers report, and the Association of Mathematics Teacher Educator's Standards for Preparing Teachers of Mathematics will be highlighted. Also, we are recruiting faculty to be piloters for the materials. To find sample materials, visit https://modules2.com/statistics/, and to indicate you are interested in piloting, please fill out the form at https://modules2.com/use-our-materials/.

For the past four years, teams of Penn State statistics and data science undergraduates have spent the summer and fall developing apps for teaching statistical concepts. Their work has culminated in over 60 apps as part of the Book of Apps for Statistics Teaching (BOAST). This webinar will share some details of the project and give some of the students the opportunity to highlight some of the newest apps they have developed. Visit: https://shinyapps.science.psu.edu

We are living in a world full of multivariate observational data. Qualitative assumptions about the data generating process, operationalised in simple directed acyclic graph can help students to understand multivariate phenomena like Simpson's or Berkson's paradox, confounding and bias. By teaching causal inference the introductory course can overcome the mantra "correlation does not imply causation". The webinar discusses some motivation as well as teaching ideas and the integration in the curriculum.

In this webinar, I will discuss a novel oral communication curriculum we developed and use with graduate students to help them communicate their scientific work with others. I'll use examples of how the students leverage elements of storytelling, stage presence, and improvisational skills to more effectively connect with and captivate audiences as they convey their research. We will also explore how these ideas can transfer into our own work, building a shared knowledge of how we can support students' (and our own) development of oral communication skills.

Think-aloud interviews with students can be used to detect specific misconceptions and understand how students reason about statistical questions. Data from think-aloud interviews can then be used to develop conceptual assessments, design new teaching strategies, or suggest further experiments to learn how students think about statistics. In this webinar, we will discuss the benefits of using think-aloud interviews to develop conceptual assessments and the experience we have had using think-aloud interviews in two introductory-level statistics courses.

In this talk, we will discuss why causal inference concepts align well with recommendations for introductory statistics courses and propose topics appropriate for such courses. In addition, we will highlight some resources for instructors interested in teaching causal inference, including a classroom activity we developed based on a popular dataset investigating the effects of youth smoking on lung function.

This work introduces new assessment tools to measure learning outcomes of students in undergraduate statistics programs (e.g. majors) against the competencies recommended in the (2014) ASA Guidelines for Undergraduate Programs in Statistical Sciences. In short, these assessment tools seek to (1) measure student learning outcomes with respect to program objectives; (2) discover whether students are gaining additional relevant competencies not explicitly included in the program/major through extracurricular experiences; (3) facilitate comparisons across years and institutions to benefit continuous improvement of the program/major. This webinar presents uses and results after piloting with Senior/Capstone undergraduate statistics students shortly before graduation at four different institutions around the US.