What kinds of models are most powerful for supporting science learning?: Models that integrate mechanism

Colloquium | November 6 | 4-5:30 p.m. | 2515 Tolman Hall

 Christian Schunn, University of Pittsburgh

 Graduate School of Education

In science, models often serve as the bridge between empirical and theoretical, what was found and what is thought to be. Mathematical and computational transformations often play a central, but perhaps partially hidden, role in this bridge. These mathematical transformations can be approached in very transactional terms, necessary evils of little theoretical value to conceptual reasoning. Or the transformations can be approached in deeply theoretical terms, as central theoretical commitments about physical objects and mechanisms. I present data arguing for the strong benefits of the latter approach in helping high school biology students come to understand underlying phenomena and solve complex problems of genetic inheritance, which includes the productive and flexible use of situation models, process diagrams, and data representations.

About the speaker: Christian Schunn is a Senior Scientist at the Learning Research and Development Center and a Professor of Psychology, Learning Sciences and Policy, and Intelligent Systems at the University of Pittsburgh. Most recently he became Co-Director of the Institute for Learning, a service organization that supports instructional reform in large school districts around the US. His current research interests include STEM reasoning (particularly studying practicing scientists and engineers) and STEM learning (developing and studying integrations of science & engineering or science & math), neuroscience of complex learning (in science and math), peer interaction and instruction (especially for writing instruction), and engagement and learning (especially in science). He is a Fellow of several scientific societies (AAAS, APA, APS) as well as a Fellow and Executive member of the International Society for Design & Development in Education.

 goldwasser@berkeley.edu