Visualizing Learning Management System data using Context-Relevant Self-Organizing Map

In the last few years, many form of Learning Management Systems (LMS) have been introduced in many educational institutions with the main objective of obtaining meaningful information from the accumulated learning data to be then utilized for increasing the quality of the educations in those institutions. One of the most popular techniques for extracting information is by visualizing the high dimensional data that characterize the information. In this study, we propose to utilize Context-Relevant Self Organizing Map, a unique visualization algorithm that preserves not only the topographical characteristics of high dimensional data but also their context, for visualizing LMS data. Our preliminary experiments with real world LMS data show that the Context-Relevant Self-Organizing map is able to provide visual information which cannot be provided by the conventional Self-Organizing Map.