The aim of this study was to determine a preferred approach to the design of word processor training programs. It examined individuals’ ability to transfer skills from one word processor to another, with the object of achieving the greatest transfer with the least retraining. Minsky’s concept of frames [1] provided the research context; this concept represents knowledge as ‘packets’ that can relate to different levels of comprehension. While lower-level knowledge tends to employ more specific detail, higher-level knowledge performs an organizing function by permitting associative recall and thus providing a generalized model.

The subjects were initially trained on WordStar and then retrained on WordPerfect. The researchers found that providing low-level information about WordPerfect and relating it to previously acquired knowledge of WordStar was the most efficient method of retraining, although this effect was not consistent over all tested functions.

The study was well constructed and invites further inquiry into whether the findings hold for retraining in other applications and for other computer skills in general. Pollock’s description of the model, however, does not sufficiently define the concepts of low and high levels of knowledge.