## 5 Experimentation and Results
### Discussion of Results  
In this section, it is highlighted the implications of using MIL to predict students' performance in a context of distance learning. Moreover, the possible limitations that the proposed approach may have are also discussed. Comparative analysis between different representations has shown that MIL robustly outperforms to traditional representation. Concretely, it is obtained approximately a 20% more accurate results on average. In the context of the problem of student's performance, it is important to attend not only to general metrics that give the same importance to all classes, but also to metrics that explain how well our system finds those problematic students that will not pass or dropout the course. This can be measured with metrics like specificity, in which MIL also outperforms \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Course**} & \multicolumn{6}{c}{**Previous Work**} & \multicolumn{6}{c}{**Proposed MIL Approach**} \\ \cline{3-10}  & & **Ref.** & **Assign.** & **Clicks** & **Demog.** & **Acc.** & **Assign.** & **Clicks** & **Demog.** & **Acc.** \\ \hline \multirow{3}{*}{Decision tree} & CCC & **FFF** & **[**40**] & X & & & 86.6\% & X & & **89.9\%** \\  & AAA & **[**44**]** & & & & & 79.4\% & X & & **93.1\%** \\ \hline \multirow{3}{*}{J48} & \multirow{3}{*}{All courses} & [30] & & X & & 86.7\% & & & & **91.9\%** \\  & & [33] & & X & & 86.5\% & & & & **90.3\%** \\ \hline \multirow{3}{*}{RandomForest} & \multirow{3}{*}{All courses} & [47] & X & X & X & 81.8\% & & & & **89.3\%** \\  & & [26] & & X & & 86.2\% & X & & & **89.3\%** \\ \hline \multicolumn{10}{c}{NaiveBayes} & All courses & [38] & & X & X & 63.8\% & X & & **84.4\%** \\ \hline \multicolumn{10}{c}{SVM} & All courses & [27] & & X & X & **88.0\%** & X & & 87.2\% \\ \hline \multicolumn{10}{c}{ANN} & All courses & [45] & X & & 89.0\% & X & & **90.9\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison with previous works that uses same algorithms from a traditional learning perspective. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Course**} & \multicolumn{6}{c}{**Previous Work**} & \multicolumn{6}{c}{**Proposed MIL Approach**} \\ \cline{2-10}  & **Ref.** & **Algorithm** & **Assign.** & **Clicks** & **Demog.** & **Acc.** & **Algorithm** & **Assign.** & **Clicks** & **Demog.** & **Acc.** \\ \hline \multirow{2}{*}{BBB} & [29] & Gaussian Mixture & & X & & 85.5\% & Multilayer Perceptron & X & & **93.9\%** \\ \hline \multirow{3}{*}{DDD} & Dynamic Incremental & & & & & & & & & \\  & Semi-supervised & X & X & X & **89.3\%** & Bagging \& RandomForest & X & & 86.8\% \\  & & Fuzzy C-means & & & & & & & & \\ \hline \multirow{3}{*}{AAA} & Convolutional and & & & & & & & & & & \\  & Recurrent Deep Model & & X & X & & 61.0\% & PART & X & & **92.0\%** \\ \hline \multirow{3}{*}{All} & [41] & Deep ANN & X & X & X & 84.5\% & & & & \\  & [39] & Recurrent Neural Network & & X & & 75.0\% & Multilayer & & & \\ \cline{1-1} \cline{2-10} courses & [45] & Adversarial Network + ANN & & X & & 89.0\% & Perceptron & & & **90.9\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison with previous works that use different algorithms from a traditional learning perspective. traditional data representation. Given the superiority in performance, it is also worth commenting on the advantage of using MIL in terms of interpretability of results. In the context of predicting student's performance, it is very important that the models are not a black box. Thus, they could be interpreted by mentors and tutors in order to be able to correct in time trends that may lead to students failing or dropping out of the course. With MIL, a student's assignment information takes up to four times less than using the simple instance-based representation. This helps to create models more quickly and reduce redundancy in the information, which makes the results easier for a human to read.