Learning and transfer of measurement tasks

Abstract

This study presents a theoretically motivated analysis of learning and performance on a micro-processor based oscilloscope. An analysis of the knowledge required to make basic measurements was done using the GOMS model and Cognitive Complexity Theory (CCT). From these analyses and the criterion used in Polson, Muncher, and Engelbeck (1986), tasks were selected for an experiment evaluating training order manipulations using naive users of oscilloscopes. Production system models for each training task were derived from CCT. The models successfully predicted transfer between tasks and training order effects. Implications for the design of systems with embedded micro-processors are discussed.