Implementation of Intelligent Tutoring System Using the Data Mining Approaches

Abstract

Web-based tutoring systems are increasingly popular due to their appeal over traditional paperbased textbooks. Whereas many tutoring systems are static HTML, the Bayesian intelligent tutoring system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences. This paper presents a Bayesian intelligent tutoring system for computer programming, called BITS. The decision making process of this system is guided by Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. The primary function of an agent is to help the user better use, manage and interact with the computer applications. Intelligent agents perform tasks for students as a dynamic, personal, and smart learning environment. In Intelligent Tutoring System, the student model has the ability of recording information on students. With this information, teaching and learning can proceed in a variety of ways based on their needs and interest.