A Measure of Human-Integrated System Performance Under Time-Varying Circumstances
There are many methods to evaluate a system from given options in discrete or fixed situations ('circumstance'). However, most systems are operated under time-varying circumstances and it's not known how to evaluate the best system design when the operator in that system moves between time varying circumstances. In this dissertation, an adaptability model has been formalized using symbolic notion, which is based on learning curve theory and the adaptability measures are proposed. In the first study ('the demonstration study'), the measures proved that they could be calculated and the learning curves could be plotted in continuous varying-circumstances. In the second study ('the empirical study'), we tested two systems under three varying-circumstances. The primary purpose of this experiment was to study whether the order and delay of changing circumstances affect the adaptability measures, in which influential circumstances are randomly arranged. The statistical tests showed that order and delay do not have effects on adaptability measures. However, the results from the graphical analysis provide useful information to adjust the setting of circumstances regarding the levels of order and delay factors in practice. The findings are expected to provide an insight into understanding how human operators adapt to changing circumstances while still continuing to achieve the goal. The results also are envisioned to provide new metrics for evaluating the effectiveness of alternatives in system design.
Landry, Purdue University.
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