## 4 Results
### Experiment 1  
In this experiment we assess the portability of prediction models between courses belonging to the same degree, having considered four different groups (Computer, Education, Engineering, and Physics). Firstly, we have obtained 24 prediction models (one for each course) and then we have tested them with the other courses' datasets of the same group, which in this case is a total of 174 numerical and 174 discretized datasets. Thus, we have carried out a total of 348 executions of J48 algorithm for obtaining each AUC value and then calculated the AUC loss versus the reference model. For the Computer group, we can observe from Table 5 that the best AUC value (0.896) when transferring a prediction model to a different course corresponds to the PM (refer to Table 1 for course names abbreviations) course model when tested against the DB course numerical dataset. However, we can observe that the overall mean value for AUC measure with discretized datasets is 0.56, which means that the predictive ability of models when used in other subjects of this group is lightly above randomness. Something similar happens with numerical datasets, where the average value is 0.57. We can also observe that the lowest (best) AUC loss in discretized dataset is close to the perfect portability (0.006). This value is obtained when using the PM model against the RE subject. Overall, we can observe that AUC loss is better in the discretized dataset than in the numerical one (0.23 versus 0.33 in average). We can also highlight that the best average values in terms of portability loss are obtained for DB course in numerical dataset and PM course in discretized datasets (0.10 in both cases). For the Education group, we can observe from Table 6 that the best AUC value (0.708) is obtained when using the prediction model of PES course against the SSCM course discretized datasets. The overall average AUC for this group's discretized dataset (0.56) is very similar to that for the numerical datasets (0.57). In addition, we noticed that portability loss (AUC loss) is near-perfect (0.003) when testing the PEPI model against HWA course dataset in the discretized datasets. The overall average portability loss for discretized dataset experiments is 0.29, much better than the mean value obtained for numerical dataset experiments (0.39). We can also highlight that the best average values in terms of portability loss correspond to PEPI course (0.30 for numerical datasets and 0.11 for discretized datasets). For the Engineering group, we can observe from Table 7 that the best AUC value (0.636) is obtained for ICS2 course prediction model when tested against ICS1 course discretized dataset. In this experiment, we can observe that the overall average value of AUC is again better in the numerical dataset (0.59) than in the discretized one (0.56), with both values staying above randomness. In addition, we can observe that the best portability loss value of 0.126 is obtained for ICS2 course model when tested against ICS1 course dataset in discretized datasets. Again, we obtain better results in the discretized than in the numerical dataset (0.24 versus 0.30) in terms of portability loss. We can also highlight that the best course average portability loss values are obtained for ICS3 in numerical dataset (0.20) and for ICS1 subject in discretized dataset (0.22). Finally, for the Physics group, we can see in Table 8 that the highest AUC value (0.641) corresponds to the MM course prediction model when tested against the MA2 course numerical dataset. This value is very close to the overall mean value for the numerical dataset (0.68), which outperforms the overall AUC mean value for the discretized dataset (0.60). If we look at the portability loss values, we notice that the best (the lowest) AUC loss value of 0.009 is obtained when testing the MM course model against MA1 course discretized dataset.