## 6 Conclusions and Future Work  
The prediction of student dropout is considered one of the main tasks in the field of educational data mining (EDM) and learning analytics (LA). This study investigated how different machine learning approaches could address this problem when the data came from three different and different-size online educational entities by using homogeneous student activity data. Our main conclusion following the study is that in general, there are few differences in performance (AUC and \(\Delta\)AUC), and some machine learning approaches perform better than others in all cases even though the target entity changes in size. Below, we summarize these differences, together with the advantages and disadvantages of each approach and some specific key findings from our experiments:  
* The individual models fit the data well at each entity. However, this requires training as many individual models as there are entities. Collecting data takes time and resources to maintain so many models. We found that these models tended to have better performance on smaller datasets, and performance may drop when entities have larger datasets. * Centralized models can build robust, generalizable models by combining data from multiple educational entities. However, in the process of data aggregation, it is critical to address privacy concerns and biases. In addition, the size of the data can influence the effectiveness of this approach: when applied to a larger dataset as a target, it often yields better results, while when applied to smaller or more specific datasets it can lead to decreased performance. It is a good option when data from different entities can be combined, so that these entities do not have to work to create and maintain predictive models. * Direct transfer has been shown to be feasible and useful in situations where the previously trained model is accurate and adequately matches the particularities of the target data. Direct transfer approaches may be influenced by the sizes of the source and target datasets, and in cases where the datasets are more similar, it seems to produce good results and minimize the duplication of effort between entities. * Repurposed transfer learning produced the best results for different sizes of entity datasets. This approach improves performance by being specialized on the target dataset without the need to have a large quantity of data. Nevertheless, the entity must have the resources to maintain this model. * Soft-voting models achieved good performance on medium- and small-sized entities, while there were greater performance differences with the largest dataset. It would be a good option when there are similarities in data sizes and context, and when entities are looking to improve performance by sharing models rather than sharing all their data. * The stacked transfer technique produced robust, consistent results in all our experiments. It outperformed the individual models, demonstrating its effectiveness in improving model generalizability and adaptability, allowing us to learn and benefit from the strengths of each individual model. This approach improves performance when entities can collaborate effectively by combining predictions from their individual models. * Federated learning addresses privacy and security issues without sharing critical data. Although there may be a slight decrease in performance, this approach stands out for its resilience and potential in environments where privacy is a priority. Similar to other approaches, the performance of federated learning can also be influenced by data size. It typically performs well with small- to medium-sized datasets while maintaining data privacy but may face challenges with large datasets due to communication and resource constraints. The results obtained in our research can be generalized to other educational contexts. Our study is based on course data from three institutions managed by the same online course provider, so they are expected to have a high similarity among them.