## 3 Proposed Approach
### Case Study
Nowadays, the collection of demographic data is a task manually performed by eighteen different teaching centers through a printed questionnaire that is sent to IFSU after completion. This process generates a series of problems, such as lack of data, reading and typing problems, and consequently low diversity and inconsistencies. These factors led to the lack of reliability in these data and their consequent non-use. Data capture consisted of collecting raw data from student interactions with Moodle VLE. The data initially had the format presented in Table 2. After selection, data were validated. This stage consisted of comparing the student situation data in the VLE to the data on the institutional academic system. Both systems are independent and have no integration. Cases of inconsistency were handled manually by checking other types of internal control. The course format analyzed in this project consists of 103 weeks divided over two years. As stated by [5], early identification of a risk situation is a fundamental criterion for its reversal. Thus, for this work, we chose to use the methodology based on [4], which consists of the application of data mining on the data of the first subjects of the course. Using this process, we chose to use data from the 50 weeks that compose the first year of the course. Every two weeks starting from the fourth, a prediction model was generated, so the approaches used in this work created 23 models in the period. After validation, data were anonymized and preprocessed, and variables were generated (features extraction). Table 3 describes the variables extracted to be used as the input for training and testing the predictive models. The table shows that all variables were based on the counting of students' interactions inside the VLE. Figure 2 exemplifies the behavior of the Weekly interactions variable for some weeks of the course and according to the Student Final Status category. Exploratory data analysis (EDA) seeks to visualize dataset information to better understand the student's behavior when using the VLE. Table 4 shows how dropout rates evolved after every 10 weeks of the course until week 50. The table also shows the dropout rates for the first and second year of \begin{table}
\begin{tabular}{l l} \hline \hline
**Column** & **Comment** \\ \hline Course & Name of the virtual classroom accessed \\ \hline Time & Day and time of the access \\ \hline IP Address & IP Address of the machine \\ \hline Full name & User (student) name \\ \hline \multirow{4}{*}{Action Event Name} & The action represents the type of interaction that the student performed in the classroom. For instance: \\  & (1) Visualization and participation on chats; \\ \cline{1-1}  & (2) Visualization and inclusion of posts in forums; \\ \cline{1-1}  & (3) Visualization of resources; and \\ \cline{1-1}  & (4) Visualization of the course. \\ \hline Description & Detailed description of the event. Example: Download the.pdf file. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information contained in the log files. the course after week 50. We considered a student as dropped out after a period of six weeks without interactions with the VLE. The idea here was to pinpoint the period where the departure occurs. The evasion rates between the two years of the course are practically the same (182 dropouts for year 1 and 172 dropouts for year 2). However, if we look proportionally at the number of students enrolled at the beginning of each year, the dropout rate is slightly higher in the second year, with 30.06% compared with 24.20% in the first year. These values differ from the average dropout rates known from higher institutions in Brazil [44] as well as from secondary and technical schools [45]. Unfortunately, there are no national data related to the distance learning modality to enable a more precise comparison. A total of 86.81% of the course dropouts of the first year are concentrated in the first 20 weeks (152 dropouts of the1 82 in the first year).