Drew presenting "Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners"
Reference: Schwarz, Christina V., Reiser, Brian J., Davis, Elizabeth A., Kenyon, Lisa, Achér, Andres, Fortus, David, Shwartz, Yael, Hug6, Barbara, Krajcik, Joe, (2009), "Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners", Journal of Research in Science Teaching, 46, 6: pg: 632-654, August, (DOI: 10.1002/tea.20311)
Abstract: Modeling is a core practice in science and a central part of scientific literacy. We present theoretical and empirical motivation for a learning progression for scientific modeling that aims to make the practice accessible and meaningful for learners. We define scientific modeling as including the elements of the practice (constructing, using, evaluating, and revising scientific models) and the metaknowledge that guides and motivates the practice (e.g., understanding the nature and purpose of models). Our learning progression for scientific modeling includes two dimensions that combine metaknowledge and elements of practice—scientific models as tools for predicting and explaining, and models change as understanding improves. We describe levels of progress along these two dimensions of our progression and illustrate them with classroom examples from 5th and 6th graders engaged in modeling. Our illustrations indicate that both groups of learners productively engaged in constructing and revising increasingly accurate models that included powerful explanatory mechanisms, and applied these models to make predictions for closely related phenomena. Furthermore, we show how students engaged in modeling practices move along levels of this progression. In particular, students moved from illustrative to explanatory models, and developed increasingly sophisticated views of the explanatory nature of models, shifting from models as correct or incorrect to models as encompassing explanations for multiple aspects of a target phenomenon. They also developed more nuanced reasons to revise models. Finally, we present challenges for learners in modeling practices—such as understanding how constructing a model can aid their own sensemaking, and seeing model building as a way to generate new knowledge rather than represent what they have already learned.