Analyzing Content Structure and Moodle Milestone to Classify Student Learning Behavior in a Basic Desktop Tools Course

This paper analyzes the content structure and Moodle milestone to classify the students' learning behavior for a basic desktop-tools on-line virtual course. The data collection phase is completed for a Learning Analytics (LA) process as a first step; by using the generated interactions among students, and with learning resources, assessments, and so on. A first exploratory data analysis study is also done with the extracted indicators (or features) of all interactions to classify them in five traits. A multidimensional parameter reduction has been implemented based on Principal Component Analysis (PCA), an example of it is also given.