Acquiring Background Knowledge for Intelligent Tutoring Systems

One of the unresolved problems faced in the construction of intelligent tutoring systems is the acquisition of background knowledge, either for the specification of the teaching strategy, or for the construction of the student model, identifying the deviations of students' behavior. In this paper, we argue that the use of sequential pattern mining and constraint relaxations can be used to automatically acquire that knowledge. We show that the methodology of constrained pattern mining used can solve this problem in a way that is difficult to achieve with other approaches.

• Type: Conference
• Year: 2008
• Where: EDM
• Authors: Cláudia Antunes
• Keywords: Data Mining, EDM 2008, Intelligent Tutoring Systems, Pattern Mining, Sequential Pattern Mining