Dynamic Measures of Potential Training Success

Abstract

The Armed Forces and other large organizations often use scores from standardized aptitude batteries as indicators of cognitive aptitude. However, aptitude may also be demonstrated by the learning that occurs during training and measured by parameters such as initial ability levels and time needed to acquire information or skills. By using computer administered Complex Experimental Learning Tasks (CELTs), learning rate parameters recently have proved to be pragmatic as well as theoretical indicators of final performance. Potential advantages of this approach include ease and economy of computer administration, testee acceptance of job relevant tests, and potential benefits of shortened training schedules. The current research compared rate measures derived from learning on four CELTs with a paper-and-pencil battery designed to include static aptitude measures of the same domains. Performance measures were computed from stimulus display times, subject response time, and item accuracy. Overall final performance was computed using the average of the last five minutes. Correlational analyses and regression indicate that, with some qualifications, learning rate measures are predictors of individual and overall levels of performance on each CELT. Implications of these findings are that the current practice of using static aptitude tests for selection to training programs may not provide the most accurate picture of an individual's potential success or failure in that program, and that, given the trend towrds new computer-assisted training technologies, individuals may be selected on the basis of their potential for rapid learning, thus making use of the least expensive and most efficient training methods possible.