# Smart System for the Retrieval of Digital Educational Content
## 4 Results
### Experimental Results  
As demonstrated previously, educational metadata describes learning content by using the labels to which they are associated. Metadata can only be used effectively by a user who has knowledge about the different standards and knows how to use them. The labeling process, which is generally manual, results in poorly described documents as their labels often miss out on the key features of the content. This makes it difficult, or impossible in some cases, to directly use that metadata in the process of searching and retrieving content. The extension of standard specification levels produces LO attributes that are either irrelevant or ambiguous and difficult to apply. A detailed study of the education sector led to the emergence of a new pruning heuristic that allows AIREH to select the most relevant metadata from the set of metadata of a given LO and quickly filter the majority of irrelevant content items by means of the Results and the Catalogue agents. Figure 4: Organizational architecture model diagram (structural view). Once this process has taken place, the remaining LOs are filtered according to a series of quality parameters of the retrieved metadata documents. After that an algorithm for selecting user-adapted content must take into account the semantics of the LO and the technical aspects of searching in the LOR, cataloguing the retrieved results automatically through intelligent mechanisms. In this work, the metadata documents were processed, and their quality level was established considering three different aspects: completeness, reliability, and accuracy of results management:  
* Completeness. The metadata of the LO must give a detailed description of its contents. Compliance with this condition will lead to a more rigorous search process, better reasoning mechanisms and a more accurate recommendation system. * Reliability. Good quality metadata labelling is essential for the optimized retrieval of content items. The metadata contains LO access information (file, metadata, and educational content) and gives the LOR that stores them a higher degree of trust in the system. * Accuracy of Results. An answer to a user request may include a large set of LOs. The classification algorithm filters the LOs in this set and sorts them according to their relevance to the user's request and the educational context of the query. Ordering the LOs according to their relevance is important because it facilitates the user's choice. Attributes associated with the user domain are important because they enable the CBR recommender system to make personalized content-based recommendations. Evaluating the Performance of the Content Retrieval Architecture  
The case study has been conducted to verify the adequacy of the proposed retrieval architecture plays and its role in the retrieval of educational content. The case study consisted of a federated search system which involved 2 LOR agents: an agent that queries user agent type, and a translator. Both LOR agents implemented the SQI client code repository access interface. Tests have been performed on two repositories, the MERLOT [65] repository (statefull) and the Learning Object Repository Network LORNET [66] (stateless), each repository has different session management. The Query Manager agent implements VSQL. The case study has been conducted in a real environment. It evaluated AIREH's efficiency in retrieving content items from two LORs. To evaluate the quality of AIREH's information retrieval, a series of evaluation metrics have been used. The determination of LO retrieval performance in the different LORs in this paper is based on the calculation of Relative recall and precision were calculated to determine the system's performance when retrieving LOs from different LORs. Moreover, we have proposed a new metric called gain, which calculates the time it takes to retrieve educational content from LOR. The gain formula is detailed below.