Proceedings of the Annual Meeting of the Cognitive Science Society

This research addresses ihc need for effective ways of teaching computer programming. It focuses on two aspects of instruction. First, the research investigates the use of analogy in teaching programming. It extends existing research by investigating what constitutes a good analogy. Second, the research investigates the effect of depth of encoding on programming performance. The factors analogy and encoding were manipulated in a 3 X 2 factorial design. Analogy was operalionalized by varying iJie clarity and systcmaticity/absu-actncss of the analogies used. Encoding was operationalized by varying the frequency with which deep encoding and elaboration of learned material were invoked by the presentation of questions on the learned material. The dependent variables were score obtained on program comprehension and program composition tasks and the time taken to perform the tasks. Research subjects were 15- to 17-year-olds without prior exposure to computer programming. Differences in mathematics ability and age were controlled. The results provide empirical support for a predictive theory of the relative goodness of competing analogies. They provide only marginal support for depth of encoding (as opcrationalizcd) in learning computer programming effectively. Post hoc data analysis suggests that good analogies assist the learning of semantics but not syntax. Furthermore, the effect of encoding was only apparent in learning syntax but not semantics.