# Smart System for the Retrieval of Digital Educational Content
## 2 Related Work  
There are many state-of-the-art developments that are based on the LO paradigm [15; 16; 17] and there are many digital content repositories that store such content through the LO format. However, neither traditional web interfaces nor conventional search engines are able to manage all the labeledLOs directly. Educators are not aware of the existence of different metadata standards and even if they were aware the content is often not labeled correctly. There is a large volume of educational content in the Web, however, standard web engines are only able to retrieve a part of the items, because they can access the visible Web but not the deep Web. In response to a user query, standard search engines make use of a centralized retrieval model that copies the information in a single centralized database and then indexes and sorts the contents by using classified documents in the database. However, this is not the case in the LORs where information can only be searched via the access mechanisms of each repository.  
The educational context generates huge volumes of digital resources. The maturity of current technology has led to a significant increase in institutional repositories which form part of the hidden network of large databases. It is, therefore, necessary to find solutions that allow for the efficient search of heterogeneous contents in a distributed context. A single repository would not provide a general knowledge base. It is, therefore, necessary to bring together all the available digital repositories, creating in this way a knowledge network with a distributed repository infrastructure. The topology of distributed repositories may vary, ranging from total decentralization, through possible intermediate situations, to absolute centralization.  
Distributed information retrieval, or federated searches [18] attempt to respond to the problem of retrieving information from the hidden Web. The main aim of a federated search is to develop models and strategies to retrieve information from distributed sources. The process is completely transparent to the user, who does not perceive the complexity involved and uniformly processes the retrieved information. The main contribution of a federated search is that the search is performed in multiple LO repositories. In addition, the location of each source is provided, and information extracted from hidden sources is controlled distributedly. Federated search is a much more complex and comprehensive centralized mechanism than traditional search engines, which do not provide real solutions to the problem of the Hidden Web.  
Federated searches can be applied in the retrieval of LOs from LORs, resolving the following three problems in the sequence in which they are given: (1) Selection of repositories, (2) Selection and retrieval of resources relevant to the query, and finally and most importantly (3) Integration of the content retrieved from multiple sources, forming a single list which is delivered to the user. Moreover, the results are personalized for each user, adapting to their needs and preferences.  
To arrive at this solution, the particularities of each of the involved elements will be described.