# Using Virtual Learning Environment Data for the Development of Institutional Educational Policies
## 3 Materials and Methods
### Data Understanding
Still the right sidebar of the graph shows the distribution of students by age, and the top bar shows the distribution by interactions. As seen, the highest concentration of interactions was found in students between 20 and 25 years of age. Another important analysis of the Figure 3 is that a significant part of the dispersion was located between 0 and 200 interactions. In this range, 52 students were identified who had 0 interactions with the VLE during the courses, of which 16 passed the course (without exam), 23 passed the exam, and 13 failed. In addition to that, only two students took the course for the first time, and both failed. Figure 4 shows the difference of interactions between students who had success versus students who failed the subjects. In the upper part of the figure, interactions are presented during the 16 weeks of the subjects, where notably the students who had success  
Figure 3: Dispersion between interactions and age. demonstrate a higher engagement in VLE compared to those who failed. The bottom part of the figure shows the total number of interactions after the end of the semester (after the 16 weeks and the final exam) and before students retake the exams. It must be noticed that the failing students had higher engagement compared to the course progress but less than the successful students. Figure 5 shows the distribution of interactions during the weeks of the course. The interactions grew until the partial exams (in weeks 8 and 14/16). This movement is an indication that the closer the exams/tests are in a given subject, the higher the students access to the VLE to consult the materials. Figure 6 shows the total number of interactions per subject (upper) and the average number of interactions for each of the analyzed subjects (bellow). It is possible to analyze that, even within a program, the use of VLE was considerably different between subjects. Analyzing the VLE subjects' didactic design, it was possible to characterize them as mainly organized as repositories of resources to support face-to-face classes, where professors upload materials, such as text, images, and videos, and provide online assessments and self-assessments. Forums are used mainly as a place for coordination and information dissemination rather than for the discussion of content-related issues. The two main uses of a quiz are as follows: first, as a form of assessment evaluation of learning instruments, generally mandatory, by a single attempt, for all active students and carried out on a pre-established date; and second, as an interactive activity oriented to education and training over a long period and allowing multiple attempts. [PERSON] et al. [38] defined a typology of didactic designs according to the type of resources and activities supported by VLE. The courses analyzed here fall mainly under the repository and self-assessment types. Figure 4: Interactions per weeks approval X failed. Figure 7 displays the frequencies of the distribution of total interactions by programs and the students' final status. In FIC and NUR, both categories have their peak of interaction near zero and do not seem to present a different distribution. On the other hand, BIO presents a different distribution of interactions between the categories, with the peak of interactions for the success category near 500 and for the failed category near 300. To evaluate whether the VLE's students' interactions were associated with their final status in the subjects, we performed a statistical analysis. First, we used the Shapiro-Wilk test to verify whether interactions from both groups (success and failure) of each course followed a normal distribution. For the groups that follow a normal distribution, we performed a \(t\)-test and for the others, we applied the Mann-Whitney non-parametric test. The goal was to check whether the means/medians (depending on the test) of the groups present statistically significant differences. This analysis was performed for three different periods of the semester: week 4, week 8, and week 16 (all weeks).