Step-Wise Evolution of Mental Models of Electric C ircuits: A "Learning-Aloud" Case Study

Various methods have been tried for fostering conceptual change in science including the use of analogies, discrepant events, and visual models. In this article we describe an approach to teaching complex models in science that takes a model construction cycle of generation, evaluation, and modification as an organizing framework for thinking about when to use each of the previous strategies. This approach of model evolution uses all of the previous methods as students are led to reassess and revise their model many times in the course of the lessons.

We reported on the case study of a student in a tutoring experiment using this approach in the study of electric circuits. We concentrated on the student's moments of surprise as motivators of conceptual change. Most of these came from discrepant events, but 1 of them appeared to come from the student's own sensed lack of coherence in an intermediate model. In this case study, the teaching method appears to lead to the construction of an explanatory model that is fairly deeply understood by the student in the sense that it can generate predictions and coherent explanations of a complex system in a transfer problem.

Some of our conclusions and hypotheses generated with respect to learning processes are as follows: (a) Discrepant events produced reactions of surprise and were eventually followed by model revisions, leading us to hypothesize a motivating and guiding role for these events; (b) the subject was able to map and apply an air pressure analogy used for electric potential and continued to exhibit traces of it through the posttest interview; (c) the subject's spontaneous use of similar depictive hand motions during the instruction and during the posttest provides initial evidence that the instruction fostered development of dynamic mental models, such as those of fluid-like flows caused by pressure differences, that can generate new mental simulations for understanding relatively difficult transfer problems. This leads us to describe the core of her new knowledge as explanatory models at an intermediate level of generality that allow her to run imagistic simulations and to hypothesize a "transfer of runnability" from the analog conception to the model in this case; (d) we hypothesize that the process underlying model generations and revisions was 1 of scaffolded abductive knowledge construction rather than induction or deduction; that evaluation and revision cycles can make up for the conjectural nature of individual abductions; and that engagement and comprehension in the cycle was fostered by small step sizes for revisions from using multiple "small" discrepant events and analogies built into the lessons.