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Despite the widespread adoption of Distance Education, the high dropout rates concern teachers and institutional managers. There are initiatives to mitigate this situation, such as applying Educational Data Mining (EDM) and Learning Analytics (LA) techniques to identify students prone to this situation. However, although effective in this task, they lack mechanisms for student motivation and teachers' pedagogical intervention since they do not present methodological proposals to encourage the learning of those identified as at risk of dropout, mitigating this possibility. The use of Active Methodologies after identifying students through EDM and LA techniques can be an effective mechanism for preventing dropout in Distance Education, expanding the potential for student engagement and collaboration. This article presents a Systematic Mapping of the Literature to identify the most used EDM and LA techniques in the context of dropout. In addition, identify the application of Active Methodologies to mitigate the possibility of dropout of courses offered in Distance Education. We evaluated 1103 articles published from January 2015 to March 2023. The results indicate a growing application of EDM and LA to identify and mitigate student dropout in Distance Education. However, studies using the pedagogical strategy of Active Methodologies to minimize this problem and enhance student retention are scarce. [ABSTRACT FROM AUTHOR]

The use of virtual environments contributes to the generation of educational data and the application of methods and techniques analysis of this data generate valuable information to educational managers. This information enables targeted and personalized decision making in order to improve student learning. The provision of intelligent services helps institutions to minimize school failures and dropouts in online courses. This article presents the results of a systematic mapping of the literature that aims to identify how the Learning Analytics (LA) and intelligent services have been applied in distance education environments (EAD). Searches were conducted from 2010 to June 2020 in the IEEE Xplore Digital Library, ACM Digital Library, Scopus, Springer and ScienceDirect. The initial search resulted in 55,832 articles and after applying the inclusion and exclusion criteria, 51 articles were selected for full reading in order to answer the research questions. The main results obtained are: of the 51 selected articles, 39% applied methods and analysis techniques in EAD; 39% offered intelligent services under the EAD and 18% applied methods and analysis techniques focused on the records of logs left by students when interacted in virtual learning environments. [ABSTRACT FROM AUTHOR]

Every classroom, whether face-to-face or EaD is characterized by the physical or virtual presence of a group of learners and a teacher or tutor. In this group of learners are people with the most diverse profiles, and one of the missions of the educator who is leading the class is to identify this profile of each student and the group. With this profile identification, it is possible for the educator adapt their teaching strategies giving greater attention to students who have more difficulties and valuing the aptitudes of each student. For example: if the student stands out for his proactivity, this characteristic must be explored. In this way, using mechanisms that support this profile identification can help teachers in this process. Considering that it is common in the classroom courses and especially in the EaD courses to make use of virtual environments of teaching, to support partially or totally the activities proposed for the class, we propose in this work the use of a model that collects information of these environments and maps to the teacher the profile of the group of learners in relation to: learners with higher and lower performance; learners with greater and lesser proactivity, reactivity and interest. [ABSTRACT FROM AUTHOR]

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