Knowledge acquisition for application : cognitive flexibility and transfer in complex content domains

Abstract

A theoretical orientation to learning and instruction in ill-structured knowledge domains is presented.The theory is especially concerned with the application of knowledge in new situations (knowledge transfer), rather than the mere reproduction of knowledge in the way that it was originally learned.It is argued that knowledge transfer in complex and ill-structured domains is centrally dependent upon "cognitive flexibility."According to the theory, greater flexibility in the representation of domain knowledge will result from approaches that promote highly interconnected rather than neatly compartmentalized or hierarchicalized mental representations; that represent knowledge in terms of multiple, rather than single, prototypes and analogies; that increase the emphasis on learning from cases, while qualifying and restricting the scope of application of abstract principles; and that rely upon situation-dependent schema assembly rather than the retrieval of a rigid, prepackaged schema.A nonlinear system of learning and instruction that promotes these requisite features of cognitive flexibility is presented.In the system, cases or examples in a conceptual "landscape" are criss-crossed in a variety of directions, along multiple dimensions.Empirical paradigms for testing the theory are presented, and positive results of preliminary experiments are discussed.Finally, issues of case selection and sequencing are addressed, and the possible role of visual-perceptual adjunct representations in making the management of complexity more tractable is highlighted.