## 4 Results
### Research Questions  
#### 4.1.1 RQ1. Which Are the Most Appropriate Datasets to Early Predict at-Risk Students? To answer this question, Table 5 provides information about the Top-5 DB-classifier combination for each week, ordered by the median. The combination that presents better results is the DB2 with AdaBoost classifier, followed by the DB5 with the same classifier in almost every semester. The exception is 2016-1, where the DB12 with AdaBoost (again) achieved the best results. However, the two previously cited combinations (DB2 and DB5) are in the second and third positions, respectively. To confirm to what extent the differences between the best performances and the others were significant, we applied the Mann-Whitney test. Table 6 shows the results of the tests for each combination. According to the results of the Mann-Whitney test, there is no significant statistical difference between the best five results. This means that one could use any of the combinations (model + DB) without loose or gain significant performances in the predictions. From now on, we will use \"DB2--AdaBoost\" as the best combination, since there was no significant difference between this one and \"DB12--AdaBoost\". We choose the former because it appears as the best combination also in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Semester** & **DB** & **Classifier** & **Median** \\ \hline \multirow{6}{*}{2016-1} & DB12 & AdaBoost & 0.62820 \\ \cline{2-4}  & DB2 & AdaBoost & 0.55128 \\ \cline{2-4}  & DB5 & AdaBoost & 0.55128 \\ \cline{2-4}  & DB9 & AdaBoost & 0.55128 \\ \cline{2-4}  & DB12 & MLP & 0.55128 \\ \hline \multirow{6}{*}{2016-2} & DB2 & AdaBoost & 0.71795 \\ \cline{2-4}  & DB5 & AdaBoost & 0.71795 \\ \cline{2-4}  & DB9 & kNN & 0.71795 \\ \cline{2-4}  & DB12 & kNN & 0.71795 \\ \cline{2-4}  & DB1 & Random Forest & 0.71154 \\ \hline \multirow{6}{*}{2017-1} & DB2 & AdaBoost & 0.66013 \\ \cline{2-4}  & DB5 & AdaBoost & 0.63072 \\ \cline{2-4}  & DB6 & kNN & 0.60784 \\ \cline{2-4}  & DB8 & kNN & 0.60784 \\ \cline{2-4}  & DB10 & Random Forest & 0.60784 \\ \hline \multirow{6}{*}{2017-2} & DB2 & AdaBoost & 0.83974 \\ \cline{2-4}  & DB5 & AdaBoost & 0.83654 \\ \cline{1-1} \cline{2-4}  & DB13 & AdaBoost & 0.75641 \\ \cline{1-1} \cline{2-4}  & DB2 & Random Forest & 0.67949 \\ \cline{1-1} \cline{2-4}  & DB4 & Random Forest & 0.67628 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Five best DB-Classifier combinations for each semester ordered (descending) by median. the other semesters (2016-2, 2017-1 and 2017-2). It is important to highlight though that from these findings, there is no better dataset configuration that one should use to train the models. We considered the best combination as DB2 with AdaBoost since it brought the best result on almost every semester. However, it is necessary to say that there is no significant statistical difference between this combination the other four on Top-5 (Table 6). So, we may say that this combination is enough to predict at-risk students. The dataset consists of interaction count, with average and median, both derived variables from the first. This dataset may have brought the best results since it has the information on interaction count and, with the other two variables, gives a notion on the behavior of the students in the past weeks, until the moment of the prediction. It can also bring some insights into student's engagement during the weeks. 1.2 RQ2. The Sole Use of the Count of Student Interactions Is Sufficient to Early Predict Students' Failure in the Course? Considering that there is no statistical difference between the models generated using DB1 and other datasets, one could say that the counting of student interactions could be sufficient to early predict student's failure. In other words, the inclusion of several different derived attributes was not sufficient to improve the performance of the models at a statistically significant level.