Data mining techniques for design of ITS student models

An Intelligent Tutoring System (ITS) is a computer system that provides a direct customized instruction or feedback to students while performing a task in a tutoring system without the intervention of a human. One of the modules of an ITS system is student module which helps to understand the student’s learning abilities. Several data mining techniques like association rule mining, clustering and mining using Bayesian networks have been proposed to design effective student models in ITS systems. This paper provides a comparative study of the various data mining techniques and tools that are used in student modeling. We also propose an example-driven approach that can integrate mined concept examples at different difficulty levels with the Bayesian networks in order to influence student learning