Separating the Knowledge Layers: Cognitive Analysis of Search Knowledge Through Hierarchical Goal Decompositions

Hierarchical goal decompositions have proved to be a useful method to make explicit the knowledge required by users to perform tasks in a wide range of applications such as computer-aided drafting (CAD) systems. This analysis method progressively decomposes a given task starting from the task layer on the top of the decomposition, to the keystroke layer at the bottom. The analysis enables a close inspection of the knowledge required to perform the task at each layer of the decomposition. In this paper we show how the method of hierarchical goal decomposition can be used to understand more precisely the knowledge that is required to perform information search tasks. The analysis pinpoints: (1) the critical strategies in the intermediate layers of knowledge that are known by experts searchers; (2) why such knowledge is difficult to acquire by novice searchers; (3) how the analysis provides testable predictions of behavior based on the acquisition of different types of knowledge. We conclude by discussing the advantages provided by hierarchical goal decompositions, and how such an approach can lead to the design of systems and training.