## 5 Discussion  
About the obtained accuracy of the student performance prediction models, as we can see in previous section tables for Experiments 1 and 2, it is noticeable that average AUC values are always a little better in the case of the numerical datasets than the discretized datasets. It is logical and expected that the models' predictive power is higher when we use numerical values. In Experiment 1, the average AUC highest values are obtained for the Physics group, having 0.68 for the numerical dataset and 0.60 for the discretized one. In Experiment 2 the highest values are found in the High group, obtaining values of 0.58 for the numerical dataset and 0.57 for the discretized dataset. Thus, in general the average AUC values are not high and only a little higher than a change (0.5). If we have a look at the maximum values for AUC, there is not a clear rule that we can obtain since we have found similar good values in both experiments: 0.89 in Computer group of experiment 1 with numerical datasets and 0.79 in medium level group of experiment 2 with discretized datasets. We can conclude that the accuracy of the prediction models when we transfer them to other different courses are not very high (but higher than a chance, AUC \(>\) 0.5), it is a little higher when using numerical values (but only slightly) and similar results are obtained in both experiments. We think that this can be in part due to the number of students vary a lot of from one course to another, ranging from 50 (minimum) to 302 (maximum) and the number of attributes vary from one dataset to another. When assessing the models' portability, we have also used the AUC loss as an indicator of portability loss. According to [PERSON] (1986), prediction models are portable as long as their portability loss values stay around 0.1 (and AUC is kept above randomness). In general, in our two experiments, we have only obtained these good values in one group, namely, the Physics group with discrete datasets with 0.60 AUC average value and 0.09 AUC loss average. Thus, this group of courses fit the [PERSON]'s rule for model portability. However, if we look at specific cases, we also found that some specific models that applied to specific courses datasets obtain good results and fit the [PERSON]'s rule. For instance, in Experiment 1, the minimum values of portability loss was 0.008 for the numerical dataset (Computer group; DB transfer to SE) and 0.006 for discretized dataset (Computer group; PM transfer to RE). In Experiment 2, the minimum value of portability loss was 0.021 for numerical dataset (Medium group; InS transfer to PESS) and 0.009 for discretized dataset (Medium group; DB transfer to PEPI). These results indicate that some particular prediction models are applicable to some other different courses. However, we are more interested in finding if a model can be correctly transferred to all the rest of the courses in its group, and thus, we have a look at portability loss average values (\"_avg_\" loss column). In this regard, we have also found some good results, and the best four prediction models are described below. In Experiment 1, we have obtained good average results for the DB prediction model in the numerical dataset (average loss of 0.10) and the MM prediction model in the discretized dataset (0.04). Some similar results were obtained in Experiment 2 with InS prediction model in the numerical case (0.12) and ICS2 prediction model in the case of discretized dataset (0.10). It is important to highlight that those best four models not only present average portability loss values close to 0.10, but they all also keep average values of AUC above randomness. Thus, it indicates that those models are portables and they can be used to correctly predict in the rest of the courses in their group and we can conclude that they meet the conditions established in the portability challenge defined by [PERSON] in The Baker Learning Analytics Prizes (1986).