Intelligent Recovery Architecture for Personalized Educational Content

Abstract

Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This work proposes a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents. The model allows the development of an open and flexible architecture that supports the services necessary to conduct a search for distributed digital content dynamically. AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is based on the proposed model; it is a multi-agent architecture that can search and integrate heterogeneous educational content through a recovery model that uses a federated search. A major challenge in searching and retrieval digital content is to efficiently find the most suitable for the users This paper proposes a new approach to filter the educational content retrieved based on Case-Based Reasoning (CBR).The model and the technologies presented in this research are an example of the potential for developing recovery systems for digital content based on the paradigm of virtual organizations of agents. The advantages of the proposed architecture are its flexibility, customization, integrative solution and efficiency.