Proceedings of the 2013 Annual Meeting of the National Association for Research in Science Teaching.
We describe model construction processes in scientifically trained experts in Part I and identify similarities to important learning and teaching processes in science class discussions in Part II. Our work with science experts has analyzed data from videotaped protocols of experts thinking aloud about unfamiliar explanation problems. These studies document the value of nonformal heuristic reasoning processes such as analogies, identification of a new variable, Gedanken experiments, and the construction and running of visualizable explanatory models. At a larger time scale, some subjects went through model evolution cycles of model generation, evaluation, and modification that utilized the heuristic reasoning processes above. In addition, the prevalence of imagistic simulation as an underlying foundation in these episodes suggests that it may be important to pay greater attention to this process in the analysis of nonformal thinking than is commonly done. To our knowledge these three levels of processes have not been emphasized in the past. They complement empirical processes of discovery, experimentation, and evaluative argumentation documented by others and provide a perspective on the nature of scientific thinking that includes the idea that model formation can involve creativity through non-empirical processes such as analogy, "running" a mental model, and Gedanken experiments. Diagrams of how the above processes interact may give us some new ways to picture the roles of nonformal reasoning and learning processes during qualitative model construction. These can be contrasted with more procedural and traditional reasoning processes of formal deduction and induction by enumeration or statistical inference. The nonformal learning and reasoning processes discussed here may be less procedural and carry less certainty than those traditional forms of reasoning, but they can be powerful engines for discovery if used within a self-correcting cycle of evaluation and modification.
These are compared to reasoning/learning processes that exemplary teachers foster in whole class discussions in physical science in Part II and some important similarities are noted. Their classes also went through cycles involved in model generation, evaluation, and modification at a macro-time-scale level. At a micro level, similar heuristic reasoning strategies as seen in the experts were used. This description at two hierarchical levels of processes helps to organize and clarify the purpose of specific, cognitively targeted teaching strategies. Techniques for diagramming discussions help to illuminate these processes as well as the role of teacher scaffolding and co-construction of the models being learned. Our rationale for the study is that comparisons to expert reasoning can sharpen our ways of describing the reasoning of students and teachers during discussions, and help in describing important teaching strategies.