# Using Virtual Learning Environment Data for the Development of Institutional Educational Policies
## 5 Discussion  
In this section, we answer the research questions proposed at the beginning of the paper. **RQ1--Is the use of VLE associated with the students' qualifications? ** Yes. We found significant statistical association between the number of student interactions within the VLE and the final status (success or fail). Moreover, after the analysis, we concluded that using only the count of the interactions inside the VLE or using only survey data to generate the predictive models led to lower model performance compared with using a combination of both. Models trained with a combination and using the count of the first four weeks (DS7) were able to achieve excellent and outstanding performances; thus, one can say that it is  
Figure 11: ROC Success in the exam. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DS** & **TP** & **TN** & **ACC** & **F1-Score** & **Precision** & **Recall** \\ \hline DS1 & 76.96 & 64.04 & 72.40 & 0.72 & 0.72 & 0.72 \\ DS2 & 88.41 & 79.00 & 85.09 & 0.85 & 0.85 & 0.85 \\ DS3 & 85.97 & 77.16 & 82.87 & 0.82 & 0.82 & 0.82 \\ DS4 & 86.98 & 76.11 & 83.00 & 0.83 & 0.83 & 0.83 \\ DS5 & 82.83 & 75.06 & 80.09 & 0.80 & 0.80 & 0.80 \\ DS6 & 87.41 & 75.06 & 83.05 & 0.83 & 0.82 & 0.83 \\ DS7 & 88.12 & 73.75 & 83.05 & 0.82 & 0.82 & 0.83 \\ DS8 & 12.58 & 94.48 & 41.48 & 0.32 & 0.65 & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predicting success in retaking the exam versus failure. possible to predict students' final status at the beginning of the courses. These findings suggest the importance of VLE in face-to-face courses, even though its usage mainly focuses on the delivery of materials and activities without much collaboration among peers. In addition, it can also be said that different VLE's activities weigh differently inside the models, as the type of interaction also increases their performances. The work of [20] used VLE data together with data collected from a student survey and evaluated the extent to which the use of the combination of both databases interfered in the models' performance. The authors concluded that there was no gain in using data collected from the survey. Moreover, the authors also tested different dataset combinations considering the different types of VLE's presence (teaching, cognitive, and social presence), according to the theory of [47], and found no statistically significant difference in the performance of the models that used this differentiation. Their results contradict the present paper's findings. Based on that, one could say that the use of different combinations of databases to improve the performances of the models as well as considering different types of interactions inside the VLE are context dependent. For the present scenario, the combination of databases and the differentiation of the types of interaction helped to improve the performance of the classifiers. **RQ2--Which features from the different databases are the most important to early predict students' performance? ** Figure 12 presents the fifteen most important attributes used by the models to predict student performance. The attribute that helped the most in predicting student performance was the number of subjects a student was enrolled in. This attribute is located in the academic system database, which was used in all possible scenarios and datasets of this study. Moreover, attributes that belong to the VLE appeared most frequently in the list (week 2, mean week 2, week 4, and mean week 4, among others). Regarding the VLE attributes, it is important to notice that Forum Week 4 appears at the seventh place of importance. As the forum is used only by the professors to communicate operational/academic/administrative things about the subjects, this attribute may indicate the importance of students being up to date about the daily routine of their courses. The importance of the types of interaction inside VLE becomes evident from the figure. Attributes Forum, Quiz, URL, and Page appear in the list of the most important attributes together with weeks 2 and 4.