## 5 Experimentation and Results
### Comparative Study
#### 5.4.2 Comparative Analysis with Previous Works  
Based on previous studies shown in Section 3 (Table 1), this work deviates from the general trend marked by the use of clickstreams (well known as student interactions with the VLE). As it has been shown, a limited number of studies use the information about assignments as influential factor for the predictions.  
However, as it has been shown in the previous comparative study, assignments can obtain equal or better results if they are processed with the appropriate representation. Thus, in this section a comparison of the accuracy to predict student's performance is carried out attending to the best MIL method according previous section, SimpleMI, and the related work. Table 16 shows these differences with a special focus on previous works that have used same algorithms but from a traditional learning perspective. Thus, it is shown the algorithm and the data from OULAD in previous work compared to the use of MIL. For example, we can focus on the unique previous work that uses solely assignments data [40]. This work is limited to courses CCC and FFF and it obtains an average accuracy of 83% using decision trees. In our work, for these courses and algorithms, the best results reached show an average accuracy of 927%. Focusing on previous works focus on predicting student success or failure in a course but not based on assignments data, the best results are achieved by [30]. This work applies J48 over VLE activity data obtaining an average accuracy over all the courses 90%. In our case, using only assignment data with the same algorithm, the best result achieved for each course gets an average accuracy of 92.7%.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Comparison** & \(R^{+}\) & \(R^{-}\) & \(p\)**-Value** \\ \hline
**SimpleMI** vs. Traditional & 273 & 3 & \(1.192\times 10^{-6}\) \\
**MIWrapper** vs. Traditional & 212 & 62 & 0.02332 \\
**SimpleMI** vs. MIWrapper & 275 & 0 & \(4.768\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Wilcoxon signed-rank test results for accuracy measure.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline Comparison & \(R^{+}\) & \(R^{-}\) & \(p\)**-Value** \\ \hline
**SimpleMI** vs. Traditional & 239 & 37 & 0.001279 \\ MIWrapper vs. **Traditional** & 0 & 276 & \(2.384\times 10^{-7}\) \\
**SimpleMI** vs. MIWrapper & 275 & 0 & \(4.768\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 15: Wilcoxon signed-rank test results for specificity measure.  
There are also some works that use algorithms not included in our study, like XGBoost ([PERSON] et al., 2017), Gaussian Mixture Models ([PERSON] et al., 2016), or Deep Learning ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). However, if their best results are compared to the best results obtained in this work, MIL achieves equals or better results using less data and in a more interpretative way. This is shown in Table 17. This table shows each previous work that employs an algorithm not included in our study and it addresses the same problem of predicting student's performance, as well as the information used, with the best result obtained using MIL (SimpleMI) for the same courses.  
Nevertheless, these comparisons should be taken carefully, since the conditions of experimentation and evaluation may differ. Thus, our study shows that general performance of predictive models based on MIL and assignments data are competitive with respect to other approaches that do not use this information or combine it with other factors. To promote future fair comparisons, all information necessary to carry out this experimental study is available in the public repository associated to the article.