ABSTRACT
Building upon recent work on production system models of transfer and analysis-based generalization techniques, we present analyses of three studies of learning to program recursion. In Experiment 1, a production system model was used to identify problem solving that involved previously acquired skills or required novel solutions. A mathematical model based on this analysis accounts for inter-problem transfer. Programming performance was also affected by particular examples presented in instruction. Experiment 2 examined these example effects in finer detail. Using a production system analysis, examples were found to affect the initial error rates, but not the learning rates on cognitive skills. Experiment 3 examined relations between the ways in which people explain examples to themselves and subsequent learning. Results suggest that good learners engage in more metacognition, generate more domain-specific elaborations of examples, make connections between examples and abstract text, and focus on the semantics of programs rather than syntax.
