Intelligent Tutoring System Design Using Markov Decision Process

Abstract

Class learning is a teaching and learning activity involving both teachers and students. Students in the class have different levels of intelligence, divided into three categories: lowest, average, and most intelligent. Teachers usually pay less attention to less intelligent students. Consider a student who is not intelligent enough to remain in school. However, if a smart student is required to repeat content he already comprehends, he will become bored. Therefore, this study developed an intelligent tutoring system (ITS)-based learning application with a Markov decision process (MDP). The combination of ITS and MDP enabled intelligence to determine the most appropriate subsequent step by delivering tutorials and examination questions that guide students to efficiently attain intelligence levels.