Visual Analysis for Monitoring Students in Distance Courses

Visual Analysis for Monitoring Students in Distance Courses

Augusto Weiand (Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil & Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul – IFRS, Brazil), Isabel Harb Manssour (Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil) and Milene Selbach Silveira (Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/IJDET.2019040102
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With technological advances, distance education has been frequently discussed in recent years. The learning environments used in this course usually generates a great deal of data because of the large number of students and the various tasks involving their interaction. In order to facilitate the analysis of the data, the authors researched to identify how interaction and visualization techniques integrated with data mining algorithms can assist teachers in predicting students' performance in learning environments. The main goal of this work is to present the results of such research and the visual analysis approach that the authors developed in this context. This approach allows data gathering on the students' interactions and provides tools to investigate and predict pass/fail rates in the courses that are being analyzed. Our main contributions are: the visualization of the resources and their use by students; the possibility of making an individual analysis of students through interactive visualizations; and the ability to compare subjects in terms of students' performance.
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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).

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