Educational Data Mining with Learning Management Systems

Abstract

This work presents the evaluation of the process of using a virtual learning platform designed ad hoc for Music Education in Secondary. Our goal is to analyze the use of this platform through the techniques of Educational Data Mining (EDM) so that we study the application of data mining to data collected with Learning Management System (LMS). These techniques allow us to understand the behavior of the data during the process of teaching and learning in online learning environments, which provides the ability to monitor activities to optimize their performance, correct learning deficiencies, detect anomalous behavior of certain groups of activity (outliers) and improve the design of online teaching-learning activities. The analytical techniques we use in research are oriented to clean the information and selection of significant variables in the study and to use cluster analysis and cluster type and predictive rules from the behavior of the data collected. The results we obtain show that the activities that have accumulated the most records have been exploratory types: “to see the subject of discussion” and “see the discussion forum”; the student body has spent most of time exploring those tools. The most intense activity has been generated by communication tools such as forums and chats where we have recorded the majority of students’ musical learning.