Main Article Content

Paula Andrea Rodríguez Marín
Departamento de Ciencias de la Computación y de la Decisión Facultad de minas - Universidad Nacional de Colombia - Sede Medellín
Colombia
Mauricio Giraldo
Departamento de Ciencias de la Computación y de la Decisión Facultad de minas - Universidad Nacional de Colombia - Sede Medellín
Colombia
Valentina Tabares
Universidad Nacional de Colombia Sede Manizales
Colombia
Néstor Duque
Universidad Nacional de Colombia Sede Manizales
Colombia
Demetrio Ovalle
Departamento de Ciencias de la Computación y de la Decisión Facultad de minas - Universidad Nacional de Colombia - Sede Medellín
Colombia
Vol. 5 No. 3 (2016), Articles, pages 21-30
DOI: https://doi.org/10.14201/ADCAIJ2016532130
Accepted: Nov 15, 2016
Copyright

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.

Downloads

Download data is not yet available.

Article Details

References

Ahmad, S., & Bokhari, M. (2012). A New Approach to Multi Agent Based Architecture for Secure and Effective E-learning. International Journal of Computer Applications, 46(22), 26–29. Retrieved from http://research.ijcaonline.org/volume46/number22/pxc3879826.pdf

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