## 6 Conclusions and Future Research  
This paper presents a detailed study about the portability of predictive models between universities courses. To our knowledge, this work is one of the first exhaustive studies about portability of performance prediction models with blended university courses, and thus, we hope that it can be of help to other researchers who are also interested in developing models for portability solutions in their educational institutions. In order to answer to our two research questions, we have carried out two experiments executing the J48 classification algorithms over 24 courses in order to obtain the AUC and AUC loss of the models when applying to different courses of the same group by using numerical and discretized datasets. Starting of the results obtained in our experiments, the answers to our two research questions are:  
How feasible it is to directly apply predictive models between courses belonging to the same degree. By analyzing the results shown in Tables 5-8, we can see that the average AUC values are not very high (both in numerical and discretized datasets), but when we used discretized datasets, the obtained models are better in terms of AUC loss or portability loss, in spite of the fact that numerical datasets present the best AUC values, which is something that we expected in advance. In fact, portability loss values are inside the interval from 0.09 to 0.28 for the discretized datasets and we obtained good portability loss results in the Computer group and in the Physics group. How feasible it is to directly apply predictive models between courses that make a similar level of usage of Moodle. By analyzing the results shown in Tables 9-11, we can see that again, the best AUC values are obtained with the numerical datasets but they are not very high. However, the best lowest portability loss values are obtained with the discretized datasets in the range from 0.22 to 0.25. In this experiment, we did not find results as good as in the first one, but nevertheless, the results obtained are inside an acceptable range. In conclusion, the results obtained in our experiments with our 24 university courses show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances. In our case, only when we have used discretized datasets and the transfer is between courses of the same degree, although  
Figure 6: Best Model of the High group with discretized dataset—Subject ICS2. only in two specific degrees of the four degrees tested, the loss portability is feasible. Additionally, we have shown the four best prediction models obtained in each experiment (1 and 2) and type of dataset (numerical and discretized). We have obtained that the most important attributes or Moodle events that appear in the decision trees are about forums, assignments, choices, resource, and page. However, it is important to remark that prediction models when using discretized datasets not only provide the lowest AUC loss values, that is, the best portability, but they also provide smaller decision trees than numerical ones and they only use two comprehensible values (HIGH and LOW) in their attributes (instead of continues values with threshold) that make them much easier to interpret and transfer to other courses. A limitation of this work is the fact that the best obtained models (decision trees) might not be directly actionable by the teachers of the other courses since those models may include activities or actions that their courses do not have. We have technically solved this problem by executing J48 as Wrapper classifier that addresses incompatible training and test data by building a mapping between the training data that a classifier has been built with and the incoming test instances' structure. Model attributes that are not found in the incoming instances receive missing values.