A state-space model for automatic instruction

Abstract

This paper introduces a state-space instructional model, in which instruction is viewed as a process of transforming the student from one state to another. The model is expressed in terms of state-space problem solving as in artificial intelligence research, an area to which computer-assisted instruction has been increasingly turning for solutions to some of its fundamental problems. The model is first described in general terms and it is explained how the usual concerns of computer-assisted instruction (such as response-sensitivity and individualisation) find expression in the model. The use of the model in the design of teaching programs is illustrated by two systems, for paired-associate teaching and concept teaching. The former involves a re-expression of familiar methods based on the use of mathematical learning models; the latter uses more heuristic methods derived from artificial intelligence. Finally, some comparisons with other instructional models are made.