Distance Education (EaD) has consolidated itself as an important modality of the educational system. Flexible, scalable and with a relatively low cost of implementation, EaD allows reaching distant locations, realizing the right to education for thousands of citizens marginalized by the absence of the State. It is a fact, however, that education must respect the individuality of the student, and it is not possible to think in terms of a merely massified educational process. In this sense, learning analysis (LA) and the mining of educational data present themselves as relevant methods of extraction and analysis of data from the teaching-learning process carried out in Virtual Learning Environments (AVA). Such methods are shown to be capable of acting in Big Data scenarios and therefore capable of conceiving and underpin initiatives to customize and adapt the teaching process. Learning in e-learning contexts characterized by the generation of large volumes of logs and statistics.