## 2 Materials and Methods
### Educational Warehouse
#### 2.1.2 Use Cases  
This proposal is set out to be used for different purposes. In this paper, we present different approaches where an educational warehouse can facilitate the management of educational data. Specifically, we propose three use cases, although the implementation of an educational warehouse is not limited to them. The first case that we present describes a development carried out at La Salle-URL, where all modules are used and new features are developed. We implemented this structure in a local environment to regulate data access securely, controlling the environment and its access. Since needs are growing, the modules described in the architecture are made in different working environments to facilitate other teams' development. At a practical level, the educational warehouse state implemented at La Salle-URL is described in Figure 3. Data are extracted from educational tools such as Moodle (LMS), third-party applications (such as Kahoot [67]) and public access (Open Data). Data are processed to reduce their volume and to get a better understanding of their nature. Once this data has been understood and adapted, it is stored in the storage module. Data related  
Figure 2: Interinstitutional use of an educational warehouse: data are exchanged between entity A and entity B, where entity A’s Data Access Interface (DAI) is the data origin of Entity B’s Extract, Transform and Load (ETL). to the operation of Moodle and data associated with the validation of an educational method used in La Salle-URL [68] are currently stored. If a request is made to access the data, an access gateway or a client is enabled so data can be accessed securely. This case is presented in detail in the Section 3. A second possible case is based on providing and managing data for research teams, which require information coming from different entities. This information may be regulated under an agreement between entities or based on open data between educational entities. A summary of the required modules can be seen in Figure 4.  
Figure 4: The educational warehouse architecture proposal for research entities working with open data: the modules represented offer the ability to progress towards the study’s objective. Figure 3: The educational warehouse architecture applied at La Salle-URL: representation of the current modules developed. This second case shows how an educational data management system allows for creating synergies in research, as it offers the possibility to create a collaborative and easily accessible environment. To comply with good practice requirements, a researcher should only adopt the educational warehouse architecture model and connect the DAI and ETL modules as the output and input gate. We believe that fields such as educational data mining or learning analytics can benefit from the synergies offered by different educational entities' collaboration. They allow the state-of-the-art to remain accessible to those interested or involved. Finally, we present a third possible case, now aimed at institutions with limitations to implement an educational warehouse at the local level. Given this architecture's modular design, it remains functional as long as the different modules operate and the established privacy principles are respected. This means that its implementation, if desired, can use cloud computing, with the benefits that this technology brings and taking into account the risks involved. In the same way, a module can be fragmented, as it is possible that at a given point there is a volume of data large enough to require the application of big data techniques in cloud computing to store and analyze, such as MapReduce, clustering or similar [69]. Components of the same module can be located in different computing environments, as shown in Figure 5.  
These three use cases, one of them already in execution and the other two drawing possible scenarios, demonstrate a solution that institutions such as universities can use to facilitate the administration, processing and regulation of educational data.