Modelling students' construction of energy models in physics |
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| Authors: | Roshni Devi Andrée Tiberghien Michael Baker Paul Brna |
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| Institution: | (1) Department of Mathematics and Computing Science, University of the South Pacific, Suva, Fiji;(2) C.N.R.S.-GRIC, COAST Research Team, Lyon, France;(3) Department of Computing, University of Lancaster, Lancaster, UK |
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| Abstract: | One of the main differences between novice and expert problem solving in physics is that novices mostly construct problem representations from objects and events in the experimental situation, whereas experts construct representations closer to theoretical terms and entities. A main difficulty in physics is in interrelating these two levels, i.e. in modelling. Relatively little research has been done on this problem, most work in AI, psychology and physics education having concentrated on how students use representations in problem solving, rather than on the complex process of how they construct them. We present a study that aims to explore how students construct models for energy storage, transformation and transfers in simple experimental situations involving electricity and mechanics. The study involved detailed analysis of problem solving dialogues produced by pairs of students, and AI modelling of these processes. We present successively more refined models that are capable of generating ideal solutions, solutions for individual students for a single task, then models for individuals across different tasks. The students' construction of energy models can be modelled in terms of the simplest process of modelling — establishing term to term relations between elements of the object/event  world and the theory/model world, with underlying linear causal reasoning. Nevertheless, our model is unable to take into account more sophisticated modelling processes in students. In conclusion we therefore describe future work on the development of a new model that could take such processes into account.1.  CHENE = CHaîne ENErgetique, or Energy Chain. (In French Chêne also means oak).2. Throughout the rest of the paper we use the following simple notation in order to avoid possible confusion between modelling as a process performed by the students, in the domain of physics, and AI modelling of the former modelling process: students' modelling in physics = modelling
SP
; AI modelling (of modelling
SP
) = modelling
AI
.3. It has not been necessary to use more sophisticated strategies at the stage of our work reported here. The next system, modelCHENE, will directly address this issue.4. Note that what we refer to in this context as problem solving may in another context be viewed as construction of a qualitative representation for subsequent quantitative problem solving.5. The protocols indicate that students use this as a kind of anchor in their reasoning-some students returning to it in order to resolve impasses. As the current problem solver provides no mechanism for handling impasses we cannot model the reuse of information in any meaningful way.6. We are grateful to an anonymous reviewer for this example.7. This provides the minimum distinction for our initial needs.8. Though there is a difficult step in deciding that the moving object really is the last unassigned object-since that requires setting aside any need to assign roles to, for example, connecting strings etc.9. At the moment we have to provide psCHENE with slightly different rulesets: providing rules with priorities would be more convenient. |
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