ABSTRACT
This research investigated how college students learned an efficient troubleshooting strategy, elimination. The subjects' task was to find the broken components in networks that were similar to digital circuits. With only minimal training in this task, subjects usually used a strategy of backtracking from the incorrect network outputs, instead of the more efficient elimination strategy, which involves backtracking but also eliminating (ignoring) components that lead into good network outputs. Computer simulation modeling suggested that in order for subjects to learn the elimination strategy on their own, they needed to apply (1) certain key domain-specific knowledge about how the components worked, and (2) the general reductio-ad-absurdum problem-solving strategy. An experiment showed that these two kinds of knowledge do enable students to increase their use of elimination, thus supporting the model.
