Martina Rau, Professor
My research focuses on learning with visual representations. Visual representations can help students learn, but they can also be confusing if students do not know how the visual representations show information. Therefore, students need representational competencies: knowledge and skills that enable them to construct, interpret visual representations, and to make connections between them. We investigate (1) which representational competencies are key to STEM learning, (2) through which processes students acquire them, and (3) best to support them in instruction.
Charles Kalish, Professor
My research focuses on inductive inference and causal reasoning: How do we predict the future and learn from experience? One line of research explores how children acquire the set of commonsense beliefs that characterize adult thinking. I am particularly interested in children’s developing appreciation of physical and intentional causality. My current research explores the role of norms in social cognition. How does children’s understanding of rules and obligations develop, and what role does such understanding play in their predictions and explanations of people’s behavior?
A second line of research addresses more general processes of categorization and inference. We explore how people use evidence to draw conclusions, and how information about sampling affects these conclusions. Ongoing studies focus on conditional probability judgments. These judgments are central to categorization and inference, and are especially interesting in stereotypes and social judgments. For example, that most basketball players are tall does not imply that most tall people are basketball players.
The ability to generalize past experience to new situations, to make inductive inferences, is central to what we think of as learning. We want children not just to be able to solve familiar problems, but also to know how to apply their knowledge in new circumstances. I hope that studying the process of generalization will tell us more about how children learn.
Visit Dr. Kalish’s lab website here.
Mitchell Nathan, Professor
I currently study how students reason quantitatively, and how their intuitions (such as invented strategies) about quantitative relations can serve as the basis for learning topics such as formal algebraic strategies. My work on student cognition includes the study of learning of mathematics and science as it occurs in career and technical education settings (a.k.a. vocational education) that can contribute to the development of effective engineering practices. I also study teachers’ beliefs about the development of students’ mathematical reasoning, how expert blind spot among educators with high levels of mathematics training may influence teachers’ views of development, and how technology that supports video case analysis and professional discourse and reflection can facilitate teacher change and professional development. My work with teachers includes those who primarily teach academic courses, and those who primarily with in technical education (pre-engineering and engineering) settings.
My research is largely rooted in cognitive, embodied and social aspects of learning and teaching behavior in and out of classrooms. I employ quantitative and qualitative research methods, such as experimental design, survey design, think aloud reports, design based research, and verbal and gesture-based analyses of learner and teacher discourse. My work is directed at both basic research on intellectual performance and learning, and applications of that work to curriculum development, teacher education and staff development.
I am currently Principal Investigator (PI) for the Institute of Education Sciences (IES) Postdoctoral Training Fellowship Program on Mathematical Thinking, Learning and Instruction at the University of Wisconsin-Madison; PI for the UW sub-award for Tangibility for the Teaching, Learning, and Communicating of Mathematics funded by National Science Foundation – Research and Evaluation on Education in Science and Engineering (NSF-REESE) for 2008 – 2013; co-PI for the National Center for Cognition and Mathematics Instruction funded by the U. S. Dept. of Education-Institute of Education Sciences (IES) for 2010-2015; co-PI for How do Instructional Gestures Support Students’ Mathematical Learning? funded by NSF-REESE for 2009-2012; co-PI for the ELViS Project (Enhancing Learning with Visual Scaffolding) funded by IES for 2006-2010; and co-PI for the AWAKEN Project (Aligning educational experiences with ways of knowing engineering) sponsored by NSF Engineering Education Program (NSF-EEP) for 2007-2011.
Sadhana Puntambekar, Professor
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.
David Williamson Shaffer, Professor
David Williamson Shaffer is the Vilas Distinguished Professor of Learning Sciences at the University of Wisconsin-Madison 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 US Peace Corps in Nepal. His M.S. and Ph.D. 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.