Visual Analysis of Educational Data: a Case Study of Introductory Programming courses at the University of Brasília.

Data visualization aims to graphically represent information from a given application domain. This approach helps in the analysis and understanding of a data set through the mechanisms of interaction and generation of graphical representations, which emphasize the observation of characteristics and patterns. In this way, this technique combined with visual learning analytic enables the detection of the expected and the discovery of the unexpected. Those have been used in numerous areas such as education, where the motivation is to understand and improve the teaching and learning processes. In the literature, data visualization is used within the educational area to predict performance and identify the student profiles, as well as monitoring educational systems in order to improve the quality of teaching. In this area, introductory computing courses stand out for the high number of students who fail or drop out of these courses. On average more than 30% of students, worldwide, drop out of introductory computing courses. At University of Brasília (UnB) this ratio is greater than 50%, which makes it an appropriate scenario for the use of data analysis and visualization techniques, in order to discover patterns related to the scenario and ways to improve the situation. This paper searches and implements the most used visualization algorithms, according to the literature, in order to assist instructors and educational managers to get information about historical and demographic data related to the course. To evaluate the visualizations, the algorithms were applied in a case study of three introductory computing courses at UnB and were evaluated through a questionnaire applied to instructors and educational managers. The results show that the respondents felt more secure when using familiar algorithms, such as pie charts and bar charts. Among the selected visualizations, sankey chart, treemap, and violin chart were the least known by the respondents. Furthermore, the bar chart was the algorithm where the information was identified quickly and correctly most of the time.