Magister – Methodology for Analysis of Distance Education Programs based on Learning Analytics
Self-Regulated Learning, Distance Learning, Learning Management System, Educational Data Mining, Learning Analytics.
This work proposes a methodology for the analysis of distance learning programs, based on learning analytics technology with the data of the students access to the Learning Management System (LMS) identifying the most frequent sequential patterns of use and classifying them according to the categories of self-regulated learning. For the sequential data mining the SPAM and VGEN algorithms was applied to the databases of two educational institutions. In addition to development of the methodology, as a result of this processing, a higher incidence of a not predicted behavior of the self-regulated learning theory was identified and to classify it was created a pattern called low participation.