Optimization method for selecting problems using the learner's model in intelligent adaptive instruction system

The purpose of our study is to develop an intelligent adaptive instruction system that manages intelligently the learner's estimated knowledge structure and optimizes the selection of problems according to his/her knowledge structures. The system adopts the dynamic problems of high school physics as a material of study, and is intended to operate on a UNIX Work Station. For these purposes, the system is composed of three parts, 1) interface part, 2) problem solving expert part, and 3) optimization expert system part for problem selection. The main feature of our system is that both knowledge structures of learner and teacher are represented by structural graph, and the problem selection process is controlled by the relationship between the learner's knowledge structure and the teacher's knowledge structure. In our system the relationship between these two knowledge structures is handled in the optimization expert system part for problem selection. In this paper the theory of the optimization expert system part for problem selection is described, and the effectiveness of this part is clarified through a simulation experiment of the originally defined matching coefficient.