The retention and school dropou of students in formal education pose important problems for society as a whole. Dropout, in particular, determines a lower intellectual and technical qualification of the population of a country, causing problems in social and economic development. On the other hand, retention often causes the impossibility of universal offer of disciplines in courses with classes that have an excess of students and, ultimately, is the first step towards evasion. In addition, both situations have a cost for educational institutions and, therefore, for the nation. Currently, many courses use Virtual Learning Environments that present a significant amount of data about student interaction. Such data can be used for the prediction of school retention, dropout and dropout. Although there is much research in the areas of Learning Analytics and Data Mining on the search and analysis of this data, it is essential that VLE environments clearly present to teachers those students who are at greater risk of dropping out or being retained. It is also accompanied that teachers can choose the algorithms they want to use without needing deep knowledge about data mining. Thus, the question that arises can be defined by the following sentence. "How is it possible to build a friendly interface that allows teachers and educational managers, from the VLE Moodle, to determine the parameters they want to use to be alerted about students at risk and evasion, as well as to present the results in an intelligible way and facilitated for this group of users? In view of the above, an attempt will be made to build a plugin in the form of a Moodle plugin, using the best data visualization and Human-computer Interaction techniques, aiming to make the choice, filtering and presentation of data on students at risk of retention , evasion and school dropout, intuitive and uncomplicated, even for those teachers and educational managers who are not familiar with technological data mining tools. The data will come from students' relaxation records in the Moodle environment and will be pre-mined in the complementary work to this one, developed by PPG colleague and research group on educational data mining, Pablo Oliveira, with the guidance of Prof. Mariano Nicholas.