Lehrer, R., & Schauble, L. (2003). Origins and evolution of model-based reasoning in mathematics and science. In R. A. Lesh & H. M. Doerr (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching (pp. 59–70). Mahwah, NJ: Erlbaum.
Models serve vital functions in scientific practice. Model-based reasoning is an instructional strategy in which students build (or draw) tangible models of their thinking and revise them over time as their theoretical understanding grows. When students build physical models and adapt them over time, models serve to help explain phenomena, develop and revise their thinking, and predict outcomes. In their chapter in Beyond Constructivism: Models and Modeling Perspectives on Mathematics Problem Solving, Learning, and Teaching, Lehrer and Schauble identify ways model-based reasoning can help children reason in mathematics. In another article, a paper for a 2013 National Science Foundation conference, Lehrer and Schauble explain how many of the same principles of modeling can be used in science.
Modeling, as conceived by both the Next Generation Science Standards (NGSS) and model-based reasoning, can be difficult for practitioners to learn simply because it involves a fundamental shift from the traditional use of models as mere displays of concepts. According to Lehrer and Schauble, three principles undergird model-based reasoning (pp. 61–62):
- Using one system of representation to represent another while keeping in mind that they remain separated
- Comparing models over time through explicit reasoning processes
- Mathematization of models: especially making them testable and generalizable
Implications for Practice
With the recent move toward integrating scientific practices into all levels of science instruction, educators and program designers will invariably think about how their theories of learning and design intersect or conflict with practice-focused instruction.
Model-based reasoning is parallel to STEM practice-focused instruction in formal and informal settings, particularly as it helps students engage with the practices of modeling and evidence-based explanation. For example, model-based reasoning pedagogy continually asks learners to form and revise representations of their thinking. They can do so with nearly any curriculum and with any medium, from paper-and-pencil drawings, modeling clay, and pipe cleaners to high-tech, software-based design tools. What modeling-based pedagogies share is a commitment to helping learners engage with models to revise and develop their processes of making sense of scientific phenomena.
For educators who are currently using model-based reasoning, incorporating scientific practices into STEM instruction is conceivably an easy shift. Educators who already know how to use models to develop student thinking over time, as Schauble and Lehrer describe, are at an advantage in implementing the modeling and explanation practices recommended by NGSS. For instructors who want to learn more about modeling, NSTA resources on modeling and The Modeling Toolkit by Windschitl and Thompson may be a helpful place to begin.
Model-based reasoning emerged from studies of educational psychology, math pedagogy, and science studies that examined how groups of people make meaning and develop new conceptual understanding together. These theories originated in science studies, with Bruno Latour and Steve Woolgar leading in conducting ethnographic studies of scientists and engineers, and Latour’s later work on how models are built in science.
Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts (2nd ed.). Princeton, N.J: Princeton University Press.
Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice (pp.19–68). Cambridge, MA: MIT Press.
Windschitl, M., & Thompson, J. (2013). The Modeling Toolkit: Making student thinking visible with public representations. The Science Teacher, 80(6), 63-69.