Decomposition and clustering analysis for students modeling in intelligent E-Learning system

Abstract

Students learn in many ways. There are all kinds of Teaching methods. When a student's learning style is compatible with the instructor's teaching style, the assumed learning outcomes will be obtained. So a good intelligent E-Learning system needs adjust teaching style according to the students' learning style. In the paper, students' modeling in intelligent e-learning system is considered. The models are based on the learning styles dimensions of the Felder & Silverman model. Students are divided into groups by decomposition and clustering method. The process is under the control of parameters such as distance and maximal number of groups. Experimental results show that a effective students' modeling can be obtained by the method.