Abstract

Initial results toward developing a unifying conceptual framework for characterizing diverse learning strategies and paradigms are presented. We outline the inferential theory of learning that aims at understanding competence aspects of learning processes, in contrast to computational theory that is concerned with complexity aspects. The theory views learning as a goal-oriented process of creating or modifying knowledge representations. Such a process may involve any type of inference (deduction, analogy or induction) or information transmutation (e.g., reformulation, abstraction or copying). Any type of learning can therefore be characterized in terms of the types of such knowledge transformations that occur in a learning process. Several concepts fundamental to understanding learning are analyzed in a novel way and compared, such as analytic vs. synthetic learning, deduction, induction, abduction, abstraction and generalization. It is shown, for example, that inductive generalization, inductive specialization and abduction can be viewed as various forms of general induction, and that abstraction is a form of constructive deduction. Based on these concepts, a general multicriterion classification of learning processes is proposed. The presented ideas have a special significance for the development of a new generation of learning systems, called multistrategy systems, that integrate diverse learning strategies in a goal-oriented fashion. 1.

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