AN EXTENDED DUAL SEARCH SPACE MODEL OF SCIENTIFIC DISCOVERY LEARNING

This article describes a theory of scientific discovery learning which is an extension of Klahr and Dunbar's model of Scientific Discovery a s Dual Search (SDDS) model. We present a model capable of describing a nd understanding scientific discovery learning in complex domains in t erms of the SDDS framework. The concepts of hypothesis space and exper iment space, central to SDDS, are elaborated and used as a representat ion of the learner's knowledge. Also, we introduce a taxonomy of searc h operations in hypothesis space which allows us to describe in detail the processes of discovery. Our ideas are tested against data of subj ects who comment on the discovery processes of a simulated learner. It is found that the conditions for performance a search operation in hy pothesis space include both sufficient knowledge of the search operati on itself and reasons for choosing a specific search operation. Furthe rmore, a number of constraints on the search in hypothesis space is di scussed: domain specific and generic prior knowledge, learning goals, and personality factors. We conclude with some recommendations for the design of discovery-based learning environments.