Cognitive components of troubleshooting strategies

Abstract

This study investigated the kinds of knowledge necessary to learn an important troubleshooting strategy, elimination. A total of 50 college-level students searched for the source of failures in simple digital networks. Production system modelling suggested that students using a common but simpler backtracking strategy would learn the more advanced elimination strategy if they applied certain domain-specific knowledge and the general-purpose problem-solving strategy of reductio ad absurdum. In an experiment, students solved network troubleshooting problems after being trained with either the domain-specific knowledge, the reductio ad absurdum strategy, both types of knowledge, or neither. Students needed both the domain-specific and general knowledge identified by the models in order to significantly increase their elimination use.

Additional information

Notes on contributors

Leo Gugerty

I would like to thank Gary Olson, Dave Meyer, and John Laird for their help in this research, and Martin Ippel, Valerie Shute, and Joshua Hurwitz for comments on the paper. This study was part of a doctoral dissertation at the University of Michigan, Department of Psychology.