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Distance Education enabled educational practices based on digital platforms. Despite its wide adoption, the high dropout rates are a reason for concern for teachers and institutional managers. There are initiatives to mitigate this situation, such as Educational Data Mining (EDM), Learning Analytics (LA), and the use of Recommendation Systems (RS). Although effective in specific aspects, these techniques lack mechanisms for the motivation of students and pedagogical intervention by teachers, as they do not present methodological proposals to encourage learning. Therefore, this article describes an RS model that presents as a differential integration of the pedagogical approach of Active Methodologies with the support of Educational Data Mining and Learning Analytics techniques to identify students with dropout risks and enhance permanence. For this, a prototype was implemented and a case study was carried out with professors from two universities to assess functionality and acceptance. According to the TAM model, more than 87% of teachers agree with the ease of use and 77% agree that RS can be useful in the teaching and learning process of students. Therefore, it can be concluded that the model contributes to teaching practices, encourages collaborative learning, and favors the monitoring of this process and the activities developed by the students. [ABSTRACT FROM AUTHOR]