An Intelligent Tutoring System with Adaptive Exercises Based on a Student’s Knowledge and Misconception

Assessment is an important part of learning that should be not only to assess students' knowledge, but also any misconceptions they might make. The teacher may have difficulty in identifying it because of the diversity of students' abilities. To solve this problem, we propose an Intelligent Tutoring System (ITS) that offers adaptive exercises. In adaptive exercises, the next problem to be solved by a student is chosen by considering her performance during learning. Unlike former ITS that is mostly based on students' knowledge, the proposed ITS uses students' knowledge and misconceptions to perform adaptation. This paper discusses the implemented learning scenario and domain model structure to support adaptive exercises. Furthermore, it discusses information about the student recorded in the student model and how it is organized and inferred to find the most appropriate problem.