CBR Proposal for Personalizing Educational Content
Fecha de publicación
Springer Science + Business Media
Advances in Intelligent and Soft Computing International Workshop on Evidence-Based Technology Enhanced Learning. pp. 115-123.
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). AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is a multi-agent architecture that can search and integrate heterogeneous educational content within the CBR model proposes. The recommendation model and the technologies reported in this research applied to educational content are an example of the potential for personalizing labeled educational content recovered from heterogeneous environments.
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