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Internet popularization and technology advances have led to the exponential growth of distance learning courses (Chaffai et al., 2017; Silva, 2015). Due to a large number of students participating in such courses, especially considering Massive Open Online Courses (MOOCs), the volume of data generated by the learning environments they use has also increased (Jing and Tang, 2017; Yuan, Powell & Cetis, 2013). However, despite the growth, these courses face a major issue: student dropout (Kampff et al., 2014; Chen and Zhang, 2017; Lykourentzou et al., 2009).
Thus, the demand for analyzing the data generated by the Virtual Learning Environments (VLE) used in this context to monitor students’ performance arises, since its large volume hinders a manual analysis (Chaffai et al., 2017, Cobos et al., 2016; Huang & Fang, 2013). Moreover, as distance learning courses become more popular and competitiveness increases, these analyses are even more crucial because they enable organizations to develop work that is more suitable to the profile of each student, as well as to monitor their development throughout the course, reducing dropout and failure rates.
Keim et al. define that “Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning, and decision making on the basis of very large and complex datasets…” (Keim et al., 2011). In this context, two processes can be identified (Andrienko et al., 2000; de Amo, 2004; Keim, 2002; Shimabukuro, 2004): knowledge discovery in databases and data visualization. Specifically, knowledge discovery is strengthened by the incorporation of visualization techniques as an instrument to stimulate more active user participation in exploration and data analysis. Accordingly, it is offered user support for monitoring, evaluation, and control of the processes, increasing the degree of confidence in the results. (Hu et al., 2017; Andrienko, 2000; Keim, 2002).
Beheshitha et al. (2016) present studies with the contribution of several authors (Kruse and Pongsajapan, 2012; Verbert et al., 2013) on the use of visual analysis approaches to assist both students’ and teachers’ learning (Molinari et al., 2016), mainly in higher education programs. The idea, in this case, is to present graphs containing data of students' interactions with the platform, such as file submission, time to perform activities, and created artifacts, among others (Govaerts et al., 2012; Leony, et al., 2012; Santos et al., 2013).