Multi-agent system for Knowledge-based recommendation of Learning Objects
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ISSN online:
2255-2863
Prefijo Doi:
10.14201
Isi Artikel Utama
Vol. 4 No. 1 (2015), Articles, pages 80-89
Abstract
Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - 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.Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - 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.
Keywords:
Clustering techniques, learning objects, metadata, multi-agent systems, recommendation systems
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References
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Sabitha, a S., Mehrotra, D., & Bansal, A. (2012). Quality metrics a quanta for retrieving learning object by clustering techniques. In 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP) (pp. 428–433). Ieee. doi:10.1109/DICTAP.2012.6215396
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Anido, L., Fernández, M., & Caeiro, M. (2002). Educational metadata and brokerage for learning resources. Computers & Education …, 38, 351–374. Retrieved from http://www.sciencedirect.com/science/article/pii/S0360131502000180
Bellifemine, F., Poggi, A., & Rimassa, G. (1999). JADE – A FIPA-compliant Agent Framework. Proceedings of PAAM. Retrieved from http://www.dia.fi.upm.es/~phernan/AgentesInteligentes/referencias/bellifemine99.pdf
Burke, R.: Hybrid web recommender systems. Adapt. web. 4321, 377–408 (2007).
Gil, A., & García, F. (2007). Un Sistema Multiagente de Recuperación de Objetos de Aprendizaje con Atributos de Contexto. ZOCO’07/CAEPIA, 1–10.
Iglesias Fernández, C. Á. (1998). Definición de una metodología para el desarrollo de sistemas multiagente. Universidad Politécnica de Madrid.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. doi:10.1016/j.patrec.2009.09.011
Li, J. Z. (2010). Quality, Evaluation and Recommendation for Learning Object. International Conference on Educational and Information Technology, (Iceit), 533–537.
Mizhquero, K., & Barrera, J. (2009). Análisis, Diseño e Implementación de un Sistema Adaptivo de Recomendación de Información Basado en Mashups. Revista Tecnológica ESPOL-RTE.
Park, Y. (2013). The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1904–1915. doi:10.1109/TKDE.2012.119
Peluffo Ordóñez, D. H. (2009). Estudio comparativo de métodos de agrupamiento no supervisado de latidos de señales ECG. Universidad Nacional de Colombia Sede Manizales.
Rodríguez M, P. A., Salazar, O., Duque, N. D., Ovalle, D., & Moreno, J. (2014). Using Ontological Modeling for Multi-Agent Recommendation of Learning Objects. In Workshop MASLE -Multiagent System Based Learning Environments, Intelligent Tutoring Systems (ITS) Conference, Hawaii, USA. Retrieved from http://iate.ufsc.br/masle/masle2014/papers/paper_7a.pdf
Rodríguez, P. A., Duque, N. D., & Ovalle, D. A. (2013). Modelo Integrado de Recomendación de Objetos de Aprendizaje. In CAVA 2013 – V Congreso Internacional de Ambientes Virtuales de Aprendizaje Adaptativos y Accesibles. (pp. 1–6).
Sabitha, a S., Mehrotra, D., & Bansal, A. (2012). Quality metrics a quanta for retrieving learning object by clustering techniques. In 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP) (pp. 428–433). Ieee. doi:10.1109/DICTAP.2012.6215396
Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27–30. doi:10.5120/7218-0024
Tabares, V., Rodríguez, P., Duque, N., & Moreno, J. (2012). Modelo Integral de Federación de Objetos de Aprendizaje en Colombia-más que búsquedas centralizadas. Séptima Conferencia Latinoamericana de Objetos Y Tecnologías de Aprendizaje, 3(1), 410–418. Retrieved from http://laclo.org/papers/index.php/laclo/article/view/40
Vekariya, V., & Kulkarni, G. R. (2012). Hybrid recommender systems: Survey and experiments. In 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP) (pp. 469–473). Ieee. doi:10.1109/DICTAP.2012.6215409
Wooldridge, M., Jennings, N. R., & Kinny, D. (1999). A methodology for agent-oriented analysis and design. Proceed-ings of the Third Annual Conference on Autonomous Agents - AGENTS ’99, 27, 69–76. doi:10.1145/301136.301165.