Representation of teaching‐world knowledge and the method of the inference for intelligent cai

Abstract

AbstractThis paper considers the learning world of differential calculus for composite functions, including trigonometric functions, and describes the mechanism of an intelligent CAI system NEUTON. In the design of the system, the teaching world is divided into the meta‐knowledge‐base for dialog management, the text knowledge‐base, the problem‐solving expert knowledge, the rule‐base for tutoring strategy, and the bug knowledge. The modular structure is thus considered for the knowledge‐base. The representation of knowledge and the method of inference are considered, utilizing the characteristics of such knowledge as follows.The text knowledge is based on the semantic network representation, where the procedural knowledge is supplemented. Other knowledge bases are described by the production rules. The knowledge structure is simplified so that the interpretation and the execution are made by a single meta‐inference engine. The response of the student as well as his level of understanding are recognized and diagnosed utilizing those knowledges, and the individual guidance process is developed. In the intelligent CAI, important mechanisms are those for identifying the cause of the error, and the determination of the micro‐ and macroscopic means of remedy from the knowledge‐base. In this paper, a schema is described where the bug generation, diagnosis mechanism, problem‐solving expert mechanism and the student model called MOD are closely interrelated.