Exploring Learning with Data Across the Curriculum and Beyond
Michelle Wilkerson (University of California, Berkeley)
Title: Exploring Learning with Data Across the Curriculum and Beyond
Abstract: With the advent of AI, automated tools, and the daily stresses of instruction, it can be difficult to remember just how powerful and surprising human learning with data can be. This talk aims to remind us of the joy of teaching, by focusing on a central question: What can data and computing allow students to think, do, and learn in their classes and in their lives?
Drawing from over a decade of research with secondary students, teachers, and undergraduates across disciplines, I will share activities and case studies where students working with data are surprised, consider new perspectives, and dig deeper. We will look at discussion protocols that invite deep reading of visualizations and the real-world issues they describe; explore student-constructed narratives that integrate statistical patterns with the human stories behind the analysis decisions; and explore case studies where unexpected findings forced students to completely rethink their hypotheses. These examples will offer a vision of what it can look like to spark joy and discovery with data, and offer concrete examples that speak directly to how other upcoming keynote themes of storytelling, instructional design, and interdisciplinarity can make an impact far beyond the statistics classroom.
Bio: Michelle H. Wilkerson is an Associate Professor in the School of Education and the Graduate Group in Mathematics and Science Education at the University of California, Berkeley where she directs the Computing, Reasoning, and Expression (“CoRE”) Lab. Wilkerson conducts basic and applied research that explores how computing practices (e.g., programming, data analysis and visualization, computer simulation, GIS mapping) are changing the ways that young people learn and communicate about our world.
Telling Stories with Data in the AI Age
Rohan Alexander (University of Toronto)
Title: Telling Stories with Data in the AI Age
Abstract: One of the joys of teaching data science and statistics is helping students discover how data can spark questions, reveal patterns, support meaningful decisions, and tell a compelling story. Yet the rapid rise of generative AI has changed how students engage with coding, analysis, and writing. Instead of asking whether AI replaces certain skills, we now face more interesting questions: How can these tools open space for deeper learning, better judgment, and genuine curiosity? How can we create opportunities for students to find joy while developing their critical thinking and technical skills in this current moment?
This keynote explores how a “Telling Stories with Data” lens can help us rethink introductory statistics and data science teaching in ways that welcome both AI-enthusiasts and AI-skeptics. We’ll look at emerging research on instructor and student attitudes toward AI, practical strategies for helping students judge data quality and ethics, and ways to help them test, verify, and refine AI-generated work. By shifting emphasis from coding first to questioning first—and from writing to editing—we can give students more room to explore, critique, and experience the wonder that drew many of us to data in the first place.
Bio: Rohan Alexander is an assistant professor at the University of Toronto, jointly appointed in the Faculty of Information and the Department of Statistical Sciences. He is interested in developing workflows that improve the trustworthiness of data science and tends to be especially focused on the role of code and testing. He co-organizes the Toronto Data Workshop, a weekly, free, online, seminar series that brings together academia and industry to share data science and AI best practice. His book, Telling Stories with Data, argues that a trustworthiness revolution is needed in data science, and proposes a view of what it could look like. He holds a PhD in Economic History from the Australian National University.
Building Human Intelligence in a World of AI: Insights from the Learning Sciences
Barbara Means (Digital Promise)
Title: Building Human Intelligence in a World of AI: Insights from the Learning Sciences
Abstract: The last four years have brought a dramatic expansion of generative AI capabilities such that powerful gen AI tools are now readily available to anyone with a smart phone. Large language models can provide fluent—and usually correct—answers to statistical problems in an instant. Is this changing everything, or changing nothing, in statistics and data science education? Within higher education systems, course learning objectives and teaching methods are slow to change, but now students have new ways of responding to them, and those behaviors are not always conducive to building understanding and enduring learning. Still, the basic nature of human learning processes, as elucidated through decades of learning sciences research, has not changed.
This keynote will discuss (1) theory-based and research-backed instructional strategies that have shown evidence of positive impacts on student engagement and learning and (2) how those strategies can be applied in a world where an AI chatbot is just one click away. Major influences on how students approach learning materials and course requirements include mindsets, task interpretation, and expectations of required effort versus potential payoff. Emerging findings show that while AI tools affect these important motivational variables, instructor practices and grading policies do as well. The keynote will propose systemic changes to both what we assess and how we assess student learning.
Bio: Barbara Means, PhD is Senior Principal Learning Sciences Researcher at Digital Promise, where she studies the implementation and effectiveness of educational innovations supported by technology. Currently, she leads large-scale studies of courseware implementation and efficacy in gateway college courses. These efforts include the Statistics Teaching and Technology Studies (STATS), which have examined teaching and learning in more than 100 introductory statistics classes taught in over 50 community and four-year colleges. A fellow of the American Educational Research Association, Dr. Means has advised the U.S. Department of Education on national educational technology plans for both K-12 and postsecondary education. She has authored or edited a half dozen books related to learning and technology research. She also has served on many study panels related to science education for the National Academies of Sciences, Engineering and Medicine, including the panels that produced How People Learn I and How People Learn II.
The Future of Statistics and Data Science Education: Findings from Recent National Academies Reports
Elizabeth Stuart (Johns Hopkins University) & Lance Waller (Emory University)
Title: The Future of Statistics and Data Science Education: Findings from Recent National Academies Reports
Abstract: How can statistics and data science educators prepare students for a rapidly evolving landscape of data and computation? Two recent National Academies consensus studies—Frontiers of Statistics in Science and Engineering: 2035 and Beyond and Developing Competencies for the Future of Data and Computing: The Role of K–12—offer forward-looking insights and recommendations with direct implications for educators at all levels.
In this session, CATS co-chairs Liz Stuart and Lance Waller will highlight key findings from these reports, discuss how they connect to current teaching practices in statistics and data science, and consider what skills, collaborations, and systems will best support learners in the coming decades.
The Committee on Applied and Theoretical Statistics (CATS) of the National Academies of Sciences, Engineering, and Medicine advises stakeholders across government, academia, industry, and nonprofit organizations on issues in statistics and data science. Through workshops, consensus studies, and related activities, CATS links research, education, and practice to address challenges of national and global importance.
Bio: Elizabeth A. Stuart, Ph.D. is the Frank Hurley and Catharine Dorrier Chair and Bloomberg Professor of American Health in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with joint appointments in the Department of Mental Health and the Department of Health Policy and Management. Her research interests are in design and analysis approaches for estimating causal effects in experimental and non-experimental studies, including questions around the external validity of randomized trials and the internal validity of non-experimental studies, as well as methods for combining data sources to assess treatment effect heterogeneity and methods for evidence synthesis. She is a member of the National Academy of Medicine and a Fellow of the American Statistical Association and the American Association for the Advancement of Science. She currently serves on the National Academies of Sciences, Engineering, and Medicine (NASEM) Committee on National Statistics (CNSTAT) and co-chairs NASEM's Committee on Applied and Theoretical Statistics (CATS).
Lance A. Waller, Ph.D. is a Professor and former Chair in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, a co-chair of National Academies’ Committee on Applied and Theoretical Statistics, and a former member of the U.S. Census Scientific Advisory Committee. Dr. Waller is a Fellow of the American Statistical Association and a Fellow of the Royal Geographical Society. His research involves the development of statistical methods for geographic data including applications in environmental health, epidemiology, disease surveillance, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley).