Predicting naval aviator flight training performance using multiple regression and an artificial neural network

The Navy needs improved methods for assigning naval aviators (pilots) to fixed-wing and rotary-winged aircraft. This study evaluated the potential of a series of single- and multitask tests to account for additional significant variance in the prediction of flight grade training performance for a sample of naval aviator trainees. Subjects were tested on a series of cognitive and perceptual psychomotor tests. The subjects then entered the Navy Flight Training Program. Subject's flight grades were obtained at the end of primary training. Multiple regression and artificial neural network procedures were evaluated to determine their relative efficiency in the prediction of flight grade training performance.;All single- and multitask test measures evaluated as a part of this study were significantly related to the primary training flight grade criterion. Two psychomotor and one dichotic listening test measures contributed significant added variance to a multiple regression equation, beyond that of selection tests ;No statistically significant differences were found between the correlation coefficients resulting from the application of multiple regression and neural network validation procedures. Both procedures predicted the flight grade criterion equally well, although the neural network applications consistently provided slightly higher correlations between actual and predicted flight grades.;The results of this study demonstrated that the single- and multitask measures accounted for added unique variance beyond that of selection tests in predicting flight grades. Since later (intermediate and advanced) flight training assignments are determined by flight grades earned during the primary portion of training, these tests could theoretically be used to predict an individual's flight grade and select aviator applicants into training pipelines prior to training.