Courtney Bell, WCER Director and Professor
Courtney Bell completed her doctorate at Michigan State University in curriculum, teaching, and educational policy after earning her BA in chemistry at Dartmouth College. A former high school science teacher and teacher educator, Courtney’s research looks across actors in the educational system to better understand the intersections of research, policy, and practice. Her studies use mixed-methods to analyze the measurement of teaching and the validity of measures of teaching quality, especially observational measures. Current and recent studies investigate how administrators learn to use a high stakes observation protocol, how raters use subject-specific and general protocols, how measures of teaching compare across countries, and the ways in which observation protocols capture high-quality teaching for students with special needs. She has published in a variety of scholarly journals and also co-edited the 5th Edition of the American Educational Research Association’s “Handbook of Research on Teaching.”
Find more information about Dr. Bell at: Wisconsin Center for Education Research
Shamya Karumbaiah, Assistant Professor
Karumbaiah studies human-centered AI for teaching and learning with the aim to augment human intelligence. Her current research focuses on constructing a scientific and critical understanding of equitable and responsible use of AI in classrooms. After being a computer scientist for over ten years, she earned a PhD in learning sciences from the University of Pennsylvania. Her dissertation empirically investigated sources of biases in AI-based learning systems. Before joining UW-Madison, she spent a year as a postdoc fellow at Carnegie Mellon University where she studied ways to augment teacher practices in human-AI partnered instruction.
Mitchell Nathan, Professor
Mitchell J. Nathan (he | him), PhD, BSEE, is the Vilas Distinguished Achievement Professor of Learning Sciences, in the Department of Educational Psychology at UW–Madison, with affiliate appointments in the Department of Curriculum and Instruction and the Department of Psychology.
Dr. Nathan is primarily driven to understand the nature of meaning and its role in knowledge, learning, and teaching in K-16 mathematics, engineering, and integrated STEM (science, technology, mathematics, and engineering) contexts. His research emphasis is on the embodied, cognitive, and social nature of knowing and instruction, such as the role of gestures during classroom teaching, learning, and assessment.
Sadhana Puntambekar, Professor, Learning Sciences Area Chair
My research interests are in the area of design and use of interactive technologies for helping middle school students learn science. For the past few years I have been working on the CoMPASS project, which aims to understand the cognitive as well as the contextual issues in integrating digital (nonlinear) text in design-based science classes. The project includes the software system CoMPASS, which uses conceptual and text representations to help students see the multiple relationships between science concepts and phenomena.
I have been studying the cognitive issues that are involved in learning from nonlinear text in which students can follow multiple paths. Specifically, I have been analyzing navigation data using the Pathfinder and k-means clustering algorithms, and then looking into audio and video data to see what may have triggered the log activity, in order to understand students’ changing representations over a period of time. I have also been examining the contextual issues such as the interplay of the roles of the teacher, peers, curriculum, and the text in the complex environment of the classroom.
My research methodology has included alternating between classroom studies and more “clinical” studies with small groups of children. While the classroom studies provide rich descriptions of the interactions between the various tools and agents, clinical studies in which students use the software individually have been valuable in understanding the factors that come into play (e.g., prior knowledge, metacognitive awareness while using traditional texts) when students process nonlinear texts that lack the global coherence of more traditional texts.
Visit Dr. Puntambekar’s research lab: Interactive Learning & Design Lab
David Williamson Shaffer, Professor
David Williamson Shaffer is the Vilas Distinguished Professor of Learning Sciences in the Department of Educational Psychology, with a focus on learning analytics, and a data philosopher at the Wisconsin Center for Education Research.
Before coming to the University of Wisconsin, Professor Shaffer taught grades 4-12 in the United States and abroad, including two years working with the U.S. Peace Corps in Nepal. His master’s and PhD are from the Media Laboratory at the Massachusetts Institute of Technology. Professor Shaffer taught in the Technology and Education Program at the Harvard Graduate School of Education, and was a 2008-2009 European Union Marie Curie Fellow.
Professor Shaffer studies how to develop and assess complex and collaborative thinking skills, with a particular interest in how students understand complex environmental issues. He is the author of “How Computer Games Help Children Learn” and “Quantitative Ethnography.”
Visit Dr. Williamson Shaffer’s research lab: Epistemic Analytics
Icy (Yunyi) Zhang, Assistant Professor
Icy Zhang received her PhD and BA in Psychology, along with a BA in Economics, from the University of California, Los Angeles (UCLA). Her research aims to move students beyond rote memorization—often resulting in rigid and fragmented knowledge—toward developing relational understanding and flexible expertise that can be applied to new learning situations. She pursues this goal through two main research streams, employing mixed methods.
First, Icy investigates the cognitive and developmental processes that underlie learning by conducting lab studies. Her work explores the effectiveness of various pedagogical tools, including embodied activities, dynamic visualizations, and relationship-mapping, to help learners develop a deeper and more robust understanding of abstract concepts in complex domains. Second, she translates these research insights into real-world educational practices by designing context-sensitive, actionable instructional solutions. She focuses on authentic learning environments, particularly in STEM fields like statistics and data science. Icy’s interest in the domain of statistics and data science stems not only from the importance of data literacy in today’s world but also from her belief that learners with limited experiences in mathematics can flourish in this domain when given the right support.