Innovative Learning


Many jobs involve troubleshooting skills, i.e., the diagnosis and repair of faults in a system. This includes: equipment technicians, engineers, mechanics, computer programmers, managers and medical professionals. While the nature of troubleshooting differs across jobs, there are some common skills and training considerations. Research on problem solving and reasoning is fundamental to understanding troubleshooting skills.

Troubleshooting can be divided into two major stages: hypothesis generation (identifying one or more potential faults) and hypothesis evaluation (testing faults and making corrections). During the first stage, the way information is perceived and organized is critical. For example, representation of the problem (i.e., schema, mental models) will determine whether it matches known faults or solution patterns. Gestalt theory which emphasizes the importance of stimulus organization is relevant to the perceptual aspect of troubleshooting, as are other theories of perception (e.g., Gibson).

During the second stage, procedural skills are needed to select and isolate the faults. Such skills can involve following or formulating rules (see ACT, repair theory, Soar, structural learning theory). Metacognition plays an important role in this stage of troubleshooting since individuals must make decisions about which strategies to use and montior how well they are working. Individual differences in specific abilities (e.g., Guilford, Sternberg) and cognitive styles can also account for variations in troubleshooting behavior.

Morris & Rouse (1985) reviewed the research on troubleshooting and concluded that: (1) troubleshooting performance degrades as the system increases in complexity or time constraints are imposed, (2) instruction in theoretical principles is not an effective way to train good troubleshooters, (3) the most effective way to ensure that a troubleshooter will employ a certain strategy is to proceduralize the task, (4) troubleshooting performance is improved when guidance on how to apply system knowledge or problem-solving strategies is explicitly provided, and (5) task-related knowledge appears to be more important than aptitude in troubleshooting performance although individuals with high intelligence are better at carrying out some strategies.

Instruction in troubleshooting should involve the following elements: (1) general understanding of system function and specific knowledge of all components, (2) knowledge of typical problems, symptoms and causes, (3) competence in use of diagnostic tools/ test equipment, (4) knowledge of appropriate data collection methods (5) knowlege of various search strategies (e.g., split/half, trace-back, functional), and (6) knowledge of specific repair or correction procedures. However, the single most important factor in troubleshooting training is extensive practice with feedback using actual or simulated systems.

A number of computer-based systems have been developed to teach students how to troubleshoot. They are described by Perez (1991). Mager (1983) discusses the development of troubleshooting training according to the principles of criterion referenced instruction .


Mager, R. (1983). Troubleshooting the Troubleshooting Course, or Debug d'Bugs. Belmont, CA: Lake Publishing Co.

Morris, N. & Rouse, W. (1985). Review and evaluation of empirical research in troubleshooting. Human Factors, 27(5), 503-530.

Perez, R. (1991). A view from troubleshooting. In M. Smith (ed), Toward a Unified Theory of Problem Solving. Hillsdale, NJ: Erlbaum.