MEASUREMENT AND PREDICTION OF COGNITIVE LOADINGS IN CORRECTIVE MAINTENANCE TASKS: II. BAYESIAN ANALYSIS OF A SAMPLE OF CLASS B ELECTRONICS TECHNICIANS' BEHAVIORS.
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES ELECTRONICS PERSONNEL RESEARCH GROUP
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Thirty-six advanced Navy electronics technicians Class B Electronics School students were given a symptom-malfunction S-M matrix completion test on a blocking oscillator circuit. Next, via a card-simulation format, each technician attempted to solve six troubleshooting problems in the same circuit records were kept of each voltage and resistance reading made and of each component replacement choice. After the troubleshooting session, the subjects took a retest on the S-M matrix completion test. Using the technicians S-M matrix values, a Bayesian computation was performed for each performance step this computation yielded Bayesian likelihoods for each replaceable component in the circuit. The Class B technicians showed notably superior performance to an earlier Class A technician sample in terms of troubleshooting time, steps to solution, and the number of correct solutions. The retest showed that as a technician works on search problems in this oscillator, he improves his original subjective S-M matrix on that circuit and appears to learn as a consequence of troubleshooting. The advanced technicians were not appreciably more Bayesian-like in component-replacement choices about 55 resembled Bayesian performances compared to 52 than the earlier Class A technician sample. The earlier finding, that S-M matrix quality is related to Bayesian-like component-replacement choices, and several competence indicators were confirmed in this study. Author
- Personnel Management and Labor Relations