Skilled Performance: Computer Software

Abstract

Training technology has traditionally addressed issues that have been limited to the acquisition of knowledge at skill levels far below those of highly skilled performers. Knowledge about the changes that occur as a result of extensive experience are potentially useful in understanding highly proficient performance. Empirical evidence suggests that experts differ from novices in the cues used to achieve a target performance. It appears that recognitional capacities of experts differ from those of novices. A Reperatory Grid Technique was the means used to investigate differences in recognitional capacities of expert and novice software specialists. Task dimensions of a variety of algorithms for the problem of critical path analysis were elicited from subject matter experts by means of a matching technique. Expert and novice computer programmers then used the empirically determined dimensions to rank nine algorithms.

Discriminant analyses performed on the data indicated successful separation of the novice and expert groups on the majority of dimensions used. Furthermore, the analyses indicated which individuals were correctly classified as experts or novices solely on the basis of scores on the dimensions. The results of this study provide support for the hypothesis that as expertise is achieved, the perceptual cues used by the proficient performer differ from those of novices.