A TASK ANALYTIC METHODOLOGY FOR PREDICTING EASE OF LEARNING OF INTERACTIVE COMPUTER SYSTEMS

Abstract

Predictive models, especially those that are based on sound theories of human behavior, represent powerful tools that can provide useful analyses of interface designs before any coding of software or empirical testing. In this research, a methodology for evaluating user interface designs in terms of ease of learning is developed and tested. The methodology represents an extension to the cognitive complexity theory of Kieras and Polson (1985). The application of this theory is well explained in (Bovair, Kieras and Polson, 1990). In the cognitive complexity models, ease of learning is measured by the learning time (or training time) which is predicted as a function of the number of new rules that must be learned. In this research, a different measure of ease of learning is proposed and different classes of rules are identified.