Publication Date


Document Type


Committee Members

Misty Blue (Committee Member), Gerald Chubb (Committee Member), Frank Ciarallo (Committee Member), Christopher Hale (Committee Member), Raymond Hill (Committee Chair), Yan Liu (Committee Member)

Degree Name

Doctor of Philosophy (PhD)


There is little argument that modern military systems are very complex, both in terms of the resources in time and money to develop them and the infrastructure that is required to produce trained operators. To properly execute human systems integration during the acquisition process, systems built to train operators must be developed that optimize training. Consequently, the training system community would benefit from simulation models that provide the ability to make accurate predictions of training processes, and allow the decision maker to specify an optimum combination of operator performance after training and the cost of that training. The goal of this research is the construction of a model of human learning using time to complete a task as a performance measure. While past research has explored the nature of functions to characterize human learning, this study will examine processes used to build a model that considers task performance as a function of training methods used to instruct a task, the nature of the task being taught, and the ability of the human to retain skill over a specified period of nonuse. An empirical study was performed to collect data from individuals completing tasks typically performed by sensor operators assigned to military unmanned aircraft systems. The tasks performed covered a range of activities that require varying combinations of human perceptual, cognitive and motor skills. The data were fitted to a set of models that were used to predict the performance outcome of a task similar in type to those used to build the model. Results are reported and recommendations for future research are offered.

Page Count


Department or Program

Ph.D. in Engineering

Year Degree Awarded


Included in

Engineering Commons