Using multiple classification ripple down rules for intelligent tutoring system's knowledge acquisition

Abstract

This research focuses on the knowledge acquisition (KA) for an intelligent tutoring system (ITS). ITSs have been developed to provide considerable flexibility in presentation of learning materials and greater abilities to respond to individual students’ needs. Our system aims to support experts who want to accumulate the classification knowledge. Rule based reasoning has been widely used in ITSs. Knowledge acquisition bottleneck is a major problem in ITSs as it is known in AI area. This problem hinders the diffusion of ITSs. MCRDR is a well known knowledge acquisition methodology and mainly used in classification domain. MCRDR is used to acquire knowledge for the classification of learning materials (objects). The new ITS is used to develop a part of online education system for the people who learn English as a second language. Our experiment results show that the classification of learning materials can be more flexible and can be organized in multiple contexts.