## 5 Experimentation and Results
### Comparative Study
#### 5.4.1 Comparative Analysis between Different Representations  
This section compares the performance of a wide set of algorithms in the problem of predicting student's success in a distance course using the same student information with different representations: traditional representation (based on single instance learning) and flexible representation (based on MIL). For solving the problem using flexible representation, as it is commented in Section 5.2, two different methods (MIWrapper and SimpleMI) that transform the MIL problem are used. The experimental study carries out a 10-fold stratified cross-validation. The full results of this experimentation can been downloaded from the web repository associated to this work ([[http://www.uco.es/kdis/mildistanceeducation/](http://www.uco.es/kdis/mildistanceeducation/)]([http://www.uco.es/kdis/mildistanceeducation/](http://www.uco.es/kdis/mildistanceeducation/)), accessed on 23 October 2021). Tables 10 and 11 show average accuracy results for each course. Thus, for each representation and algorithm, it is presented the average accuracy results of each course presented in OULAD. It can be observed that SimpleMI (using flexible representation) obtains the highest accuracy for most algorithms in the different courses. Thus, with an accuracy between 85% and 95% for all algorithms, SimpleMI outperforms in a robust way to traditional representation. MIWrapper (also using flexible representation) achieves similar results to SimpleMI and it obtains better results for the most algorithms than traditional representation. Although its values are somewhat lower. This affects to general accuracy of MIWrapper (around 80%) that is lower than those of SimpleMI. Algorithms that use traditional representation have a more variable performance. It can be appreciated that in general, this representation obtains lower accuracy (around 65%). In this case, we appreciate that more complex algorithms like the multi-layer perceptron, LibSVM or Ridor are needed to reach results comparable to SimpleMI. This is a disadvantage in terms of interpretability, since these methods do not provide information of which are the most relevant attributes in order to obtain representative information to help students. In this line, methods like those based on rules or trees get to outperform their results using SimpleMI representation while maintaining interpretable results. Concretely, they  
obtain a 20% more than accuracy in average, using the same data but with a more optimal representation that fixes better to the problem. For a more detailed analysis, Table 12 shows the average results for accuracy, sensitivity and specificity considering average results of the seven courses by the different algorithms. Results are grouped by representation: traditional representation and flexible representation (SimpleMI and MIWrapper). A full report of the results can be seen at the web repository associated to the article ([[http://www.uco.es/kdis/mildistanceeducation/](http://www.uco.es/kdis/mildistanceeducation/)]([http://www.uco.es/kdis/mildistanceeducation/](http://www.uco.es/kdis/mildistanceeducation/)), accessed on 23 October 2021). These data help to see in more detail tendencies like the superiority of SimpleMI, that gets the best accuracy results in all courses. Thus, the general tendency is that flexible representation (using SimpleMI) gets to improve the algorithms performance, obtaining better accuracy values versus traditional representation. In addition, this table shows in-depth the differences of performance between methods with the different representations. On the one hand, it is shown that traditional representation causes that algorithms obtain better values for specificity (predicting students that do not pass the course) at the expense of obtaining worse values for sensitivity (predicting students that pass the course).