Proposal of a problem space exploration model based on knowledge structures and constraint consideration update formulas for arithmetic word problems

Abstract

The authors have proposed a knowledge structure for arithmetic sentences and have attempted to estimate the learner's comprehension state based on this structure. The construction of a learner model that represents the learner's comprehension state is an important element in the study of Intelligent Tutoring System. machine learning methods such as Knowledge Tracing describe the learner's state transitions using probabilistic models, while the problem exploration process is not explicitly not explicitly described. In contrast, this study defines problem making as a search of the problem space based on knowledge structures and expresses these transition rules by the degree to which they take into account the constraints of the knowledge structures. This is a new understanding state estimation model that combines a semantic description of search with a quantitative update formula for the degree of consideration. This paper reports the implemented search model and its simulated results based on real logs.