Educational Resources Recommendation System for a heterogeneous Student Group
Abstract In a face-class, where the student group is heterogeneous, it is necessary to select the most appropriate educational resources that support learning for all. In this sense, multi-agent system (MAS) can be used to simulate the features of the students in the group, including their learning style, in order to help the professor find the best resources for your class. In this paper, we present MAS to educational resources recommendation for group students, simulating their profiles and selecting resources that best fit. Obtained promising results show that proposed MAS is able to delivered educational resources for a student group.
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Duque, N., Tabares, V., & Vicari, R. (2015). Mapeo de Metadatos de Objetos de Arendizaje con Estilos de Aprendizaje como Estrategia para Mejorar la Usabilidad de Repositorios de Recursos Educativos. VAEP-RITA, 3(2), 107–113.
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Kaššák, O., Kompan, M., & Bieliková, M. (2015). Personalized hybrid recommendation for group of users: Top-N mul-timedia recommender. Information Processing & Management, 000, 1–19. https://doi.org/10.1016/j.ipm.2015.10.001
Li, J. Z. (2010). Quality, Evaluation and Recommendation for Learning Object. International Conference on Educational and Information Technology, (Iceit), 533–537. https://doi.org/10.1109/iceit.2010.5607654
Mizhquero, K., & Barrera, J. (2009). Análisis, Dise-o e Implementación de un Sistema Adaptivo de Recomendación de Información Basado en Mashups. Revista Tecnológica ESPOL-RTE.
Othman, N., & Amiruddin, M. H. (2010). Different perspectives of learning styles from VARK model. Procedia - Social and Behavioral Sciences, 7(2), 652–660. https://doi.org/10.1016/j.sbspro.2010.10.088
Pe-a, C. I., Marzo, J., De la Rosa, J. L., & Fabregat, R. (2002). Un sistema de tutoría inteligente adaptativo considerando estilos de aprendizaje. Universidad de Girona, Espa-a.
Rodriguez, P., Tabares, V., Duque, N., Ovalle, D., & Vicari, R. (2013). BROA: An agent-based model to recommend rel-evant Learning Objects from Repository Federations adapted to learner profile. International Journal of Interac-tive Multimedia and Artificial Intelligence, 2(1), 6. https://doi.org/10.9781/ijimai.2013.211
Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27–30. https://doi.org/10.5120/7218-0024
Alonso, C., Gallego, D., & Honey, P. (1997). Los Estilos de Aprendizaje. Procedimientos de diagnóstico y mejora. Bil-bao.
Boratto, L., & Carta, S. (2010). State-of-the-art in group recommendation and new approaches for automatic identifica-tion of groups. Studies in Computational Intelligence, 324, 1–20. https://doi.org/10.1007/978-3-642-16089-9_1
Duque, N., Tabares, V., & Vicari, R. (2015). Mapeo de Metadatos de Objetos de Arendizaje con Estilos de Aprendizaje como Estrategia para Mejorar la Usabilidad de Repositorios de Recursos Educativos. VAEP-RITA, 3(2), 107–113.
Elahi, M., Ricci, F., & Massimo, D. (2014). Interactive Food Recommendation for Groups, 6–7.
Fleming, N., & Baume, D. (2006). Learning Styles Again: VARKing up the right tree! Educational Developments, (7). Re-trieved from http://www.johnsilverio.com/EDUI6702/Fleming_VARK_learningstyles.pdf
Kaššák, O., Kompan, M., & Bieliková, M. (2015). Personalized hybrid recommendation for group of users: Top-N mul-timedia recommender. Information Processing & Management, 000, 1–19. https://doi.org/10.1016/j.ipm.2015.10.001
Li, J. Z. (2010). Quality, Evaluation and Recommendation for Learning Object. International Conference on Educational and Information Technology, (Iceit), 533–537. https://doi.org/10.1109/iceit.2010.5607654
Mizhquero, K., & Barrera, J. (2009). Análisis, Dise-o e Implementación de un Sistema Adaptivo de Recomendación de Información Basado en Mashups. Revista Tecnológica ESPOL-RTE.
Othman, N., & Amiruddin, M. H. (2010). Different perspectives of learning styles from VARK model. Procedia - Social and Behavioral Sciences, 7(2), 652–660. https://doi.org/10.1016/j.sbspro.2010.10.088
Pe-a, C. I., Marzo, J., De la Rosa, J. L., & Fabregat, R. (2002). Un sistema de tutoría inteligente adaptativo considerando estilos de aprendizaje. Universidad de Girona, Espa-a.
Rodriguez, P., Tabares, V., Duque, N., Ovalle, D., & Vicari, R. (2013). BROA: An agent-based model to recommend rel-evant Learning Objects from Repository Federations adapted to learner profile. International Journal of Interac-tive Multimedia and Artificial Intelligence, 2(1), 6. https://doi.org/10.9781/ijimai.2013.211
Sikka, R., Dhankhar, A., & Rana, C. (2012). A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications, 47(9), 27–30. https://doi.org/10.5120/7218-0024
Rodríguez Marín, P. A., Giraldo, M., Tabares, V., Duque, N., & Ovalle, D. (2016). Educational Resources Recommendation System for a heterogeneous Student Group. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 5(3), 21–30. https://doi.org/10.14201/ADCAIJ2016532130
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