Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)