## 3 Proposed Approach
### Case Study  
The case study consisted of the following steps: Data capture, data pre-processing, data understanding, and modelling, according to the solution proposed in the Figure 1. These steps occur in parallel, with tests, implementations, and generation of new features for developing models for the early prediction of at-risk students in a technical distance learning course. The methodology to generate the models relies on the counting of interactions of the students inside the VLE, with the use of the proposed solution described in the previous section. Data related to the student's interactions were collected from the logs of the institutional Moodle platform of a given technical distance course of the Instituto Federal Sul Rio-grandense (IFSuI) in Brazil. Table 1 shows the number of logs collected, the number of students enrolled in the course, and the percentages of dropout and success. The course is taught in 18 different cities throughout the state of Rio Grande do Sul and involves weekly activities that are posted on the VLE by the teacher. Students have one week to develop the activities with the help of tutors. The course has a maximum completion time of 103 weeks, with a total workload of 1215 h divided into disciplines. The maximum duration is 24 months, with three breaks also called vacations, and the student's final situation is determined by their performance in the evaluations and their re-enrolment every six months. The maximum term for completion of the curriculum is four years, and the student may repeat each discipline only once and, therefore, the year. The student has the option of taking up to two subjects for the next year and taking them concurrently with the others. For approval, the student must have a grade of six or higher in each of the disciplines of the curriculum. Students who spend 365 days without interactions with the virtual environment or do not perform their annual re-enrolment are considered absent and are removed from the course. Thus, the student receives a grade from 0 to 10 at the end of a given discipline, and one of two states is associated with the student: Approved or failed. However, we aimed to predict students who drop out during the course. For this, the student will be considered dropped out if they leave, do not perform the activities during the course, and their enrolment in the following semester. The choice to only use data from the counting of interactions was motivated by previous research that achieved satisfactory results using the same approach [23, 25]. This choice was also related to limitations on capturing other kinds of data for the present study. In previous works, we sought to create models that are easy to generalize so that they could be applied to other courses. To accomplish \begin{table}
\begin{tabular}{c c c c} \hline
**Number of Log Rows** & **Number of Students** & **Dropouts (\%)** & **Success (\%)** \\ \hline
1,051,012 & 752 & 354 (47\%) & 398 (53\%) \\ \hline \end{tabular}
\end{table} 
Table 1: Dataset summary. Figure 1: Proposed approach. that, we used four courses, where the model created by one was applied to the others, and the models generated with data from three courses were applied to the remaining one. In these experiments, the labeling of the type of interaction was tested and did not show significant results. When testing the models generated with data from one course on data from other courses, this type of labeling negatively impacted the results. Studies such as [PERSON] et al. [39] tested the application of different types of interactions and derived data, with their labeling showing no significant differences in performance. Thus, we applied the methodology that presented the best previous results to model other courses in the same educational context, even if the model is derived from data from one course only. The courses studied here are offered in several cities throughout the interior of Brazil and present a large demographic diversity.