Abstract
Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.
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Acknowledgments
The research presented in this paper was partially funded by the COLCIENCIAS project entitled: “RAIM: Implementación de un framework apoyado en tecnologías móviles y de realidad aumentada para entornos educativos ubicuos, adaptativos, accesibles e interactivos para todos” of the Universidad Nacional de Colombia, with code 1119-569-34172. It was also developed with the support of the grant from “Programa Nacional de Formación de Investigadores – COLCIENCIAS”. Authors thank to Prof. Diego H. Peluffo-Ordóñez as well as PhD. student Juan C. Alvarado-Pérez from Universidad Cooperativa de Colombia-Pasto for provided discussion and contributions on data mining.
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Rodríguez, P., Duque, N., Ovalle, D.A. (2015). Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection. PAAMS 2015. Communications in Computer and Information Science, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-19033-4_31
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DOI: https://doi.org/10.1007/978-3-319-19033-4_31
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