One of the digital technologies used in current practices of online education is the Virtual Learning Environment (VLE). Educators use these environments to provide online information, but have little support to assess the learning of learners at a distance, so that the lack of perception of the educator as to the state of understanding of their students can lead tofailure of an online course.
Most VLEs store large volumes of data from the history of accesses to system resources made by students, their assessments, among others. In recent years the Educational Data Mining has been used to explore the data from educational settings, as well as better understand the students and their teaching and learning.
The objective of this work is to assess online learning by the use of Educational Data Mining on the LabSQL virtual environment used in the Federal University of Pará, through the application of techniques called Decision Tree, Bayesian Network, Association Rules and Cluster Analysis.
The results obtained proved to be efficient to support educators in the assessment of online learning because they allow the analysis of student’s profile regarding the use of this technology and the teaching-learning environment in LabSQL. Moreover, the rules generated from data mining indicate how the student can improve learning by better using the environment.
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