Abstract
Due to the growing accessibility of high speed internet, the increasing of computational performance of personal computers and the decreasing cost for data storage, in the last few years we have seen the proliferation of Learning Management Systems (LMS) to support e-learning. The original goal of LMS is to enable educational institutions to efficiently acquire learning data and utilize them for designing efficient teaching strategies. However, analyzing these data is often prohibitively difficult due to their complexity and volume. In this research, we develop an analytical method for extracting cluster characteristics of students from their learning data. The proposed method efficiently combines clustering algorithm with visualization method and can be applied to general learning data.
