Dynamic mental models in learning science: The importance of constructing derivational linkages among models

Abstract

We present a theory of learning in science based on students deriving conceptual linkages among multiple models which represent physical phenomena at different levels of abstraction. The mod- els vary in the primitive objects and interactions they incorporate and in the reasoning processes that are used in running them. Students derive linkages among models by running a model (embodied in an inter- active computer simulation) and reflecting on its emergent behaviors. The emergent properties they iden- tify in turn become the primitive elements of the more abstract, derived model. We describe and illustrate derivational links among three models for basic electricity: a particle model, an aggregate model, and an algebraic model. We then present results of an instructional experiment in which we compared high school students who were exposed to these model derivations with those who were not. In all other respects, both groups of students received identical instruction. The results demonstrate the importance of enabling stu- dents to construct derivational linkages among models, both with respect to their understanding of circuit theory and their ability to solve qualitative and quantitative circuit problems. © 1999 John Wiley & Sons, Inc. J Res Sci Teach 36: 806-836, 1999