ABSTRACT
This chapter shows that people often generate problem-solving strategies in a new domain by processing example solutions. However, this approach presumes the existence of some related strategies for processing examples in that new domain. The question then becomes: Where do those strategies come from? An important aspect of processing examples is knowing which features of the example problems are structurally significant and which features are superficial. Indeed, much research on expert-novice differences highlights experts’ great advantage in properly representing and categorizing problems in terms of deep features. But how can problem solvers new to a domain achieve this skill? In many domains, solvers have preconceptions of which features are significant and which are superficial. When these preconceptions are on target, i.e., the presumed-significant features are indeed relevant to the solutions and the presumed-superficial features are irrelevant, then learning by example can proceed effectively and efficiently.
