New light on novice—expert differences in physics problem solving

It is now widely accepted that intelligent problem solving requires access to a source of domain‐specific knowledge, and the existence of a control system to constrain the manner in which domain knowledge is searched. An example of a domain which has been intensively investigated is problem solving in physics. A number of researchers have proposed mutually incompatible theories to explain the search control strategies used in this area. This paper looks at backward inference, schema‐guided forward inference, and planstacking (a form of meta‐level backward inference followed by forward inference at the object level). It also considers a proposal to introduce neural network modelling into the short‐term memory component of a production rule‐based problem solver. One widely reported finding (Larkin, McDermott, Simon & Simon, 1980), is that novice problem solvers in the domain of physics use backward inference as a search control technique, while experts use forward inference. The backward‐to‐forward inference shift has indeed become so entrenched that attention has already shifted from whether to how it occurs. In spite of its widespread acceptance, the empirical basis for this claim is somewhat fragile, and a major aim of the present study was to re‐examine it using much larger samples of experts and novices than have been employed in the past, and a methodology which does not depend upon protocol analysis. Our data show that both experts and novices exhibit a forward inference rather than a backward inference order of equation generation. Experts were more likely to be able to plan their solutions at a descriptive meta‐level than novices. While existing theories are able to account for some of these findings, none of them is completely satisfactory in explaining the full range of data.