Student Behavior Analysis Using Self-organizing Map Clustering Technique

E-learning is the resulting product from the evolution of internet technology. It acts as a medium of learning virtually without limitation of time and space and the need for teachers to be present physically. Currently, Moodle which is a learning management system has become an important medium to deliver e-learning easily by providing customized tool for educators to deploy learning materials in various forms, provide online discussion forum, online quizzes, online assignments and online activities among students. Moodle capture the student’s interactions and activities while learning on-line using the log files. The data stored in the log files contain meaningful information such as the student’s behavior, preferences and knowledge level. This information is very useful for educators to analyze the student’s characteristics in order to improve the teaching methods. In addition, the student’s progress can be improved by overcome the problem of one-size-fits-all and also to improve student learning experienced while using the system. In this paper, the student’s action and behavior while using e-learning system are analyzed in order to identify the significant pattern of the student’s behavior using Self-Organizing Map (SOM) clustering technique. The ability of SOM to analyze large amounts of data with variety types of variables and with better visualization of the result give an advantage to this technique. The experiment shows that unsupervised learning using SOM is able to identify several clusters from the student’s behavior by visualization of high dimensional data into two-dimensional (2-D) space.