## 5 Discussion  
Our experiments in this case study indicate that the different machine learning approaches produced subtle differences in performance with respect to the baseline local models. Additionally, each method had its own set of advantages and disadvantages. Therefore, entities or institutions must decide carefully in order to achieve a certain degree of improvement, or if they are interested in reducing effort and resources. Overall, our findings underscore the effectiveness of the repurposed transfer and ensemble stacking approaches in terms of performance. These approaches showed high promise and consistently outperformed our baseline. The stacking technique especially stood out, significantly improving on the performance of individual models in almost all cases. However, we note that this technique did not clearly improve performance when working with a small dataset as the target, suggesting that its application may depend on the size and/or context of the available data. The repurposed model also provided notable results in all tests, outperforming the benchmark model in most cases. However, its performance may decrease on smaller datasets, which could be due to a lack of sufficient data for the pretrained model to adequately fit local features. These two approaches, stacking and repurposed, improved the results of the individual models, but it is important to bear in mind that these methods include extra resources, as they require the implementation of additional models or extra training for the transfer process. On the other hand, while the centralized approach generally produced good results, it did not always outperform the individual model, for instance when applied to a smaller dataset as the target. This may be because the diversity and complexity of the data in larger datasets may exceed the generalization capabilities of the centralized model. It exhibited improved performance when used on the medium-size dataset but faced challenges when applied to smaller-size datasets, where differences between entities may have influenced its ability to generalize. The least positive results were from the direct transfer approach, which was expected, as the entities were different. However, it is interesting to note that direct transfer performed remarkably well when transferring data from the largest dataset to the second dataset, suggesting that direct transfer can be effective in certain cases. As for the voting approach, while it did not perform universally well, it did perform relatively well when applied to medium- or small-size datasets as the targets. This may be because the differences between individual models were less noticeable, and the weighted aggregation of models may have improved the robustness of the final result. Although federated learning demonstrated a low level of performance, it is a viable option, especially in contexts where the privacy of student data must be preserved. Its ability to outperform direct transfer in terms of performance reinforces its importance, although its performance was found to be more stable on small- and medium-sized datasets. Our results confirm the previously obtained results in different related works that have compared different machine learning approaches. In some studies [18; 21; 27], the performance of direct transfer learning models against individual models has been exploited. These studies reveal that direct transfer methods retain most of their predictive power when contexts are similar, but they do not match the performance of individual models. Some works [17; 20] compared individual, direct, and centralized approaches, showing that a centralized approach obtained better results than the direct transfer, but individual models were the best ones. Individual, direct transfer, soft voting, and stacking ensemble transfer were examined in [16], where similar results to individual models were obtained with soft voting and stacking, with a particular mention for the stacking method, which often exhibited improvements.