Advertisement

Adaptive and Personalized Educational Ubiquitous Multi-Agent System Using Context-Awareness Services and Mobile Devices

  • Oscar M. Salazar
  • Demetrio A. OvalleEmail author
  • Néstor D. Duque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9192)

Abstract

In the last decade, some useful contributions have occurred to e-learning system development such as adaptation, ubiquity, personalization, as well as context-awareness services. The aim of this paper is to present the advantages brought by the integration of ubiquitous computing along with distributed artificial intelligence techniques in order to build an adaptive and personalized context-aware learning system by using mobile devices. Based on this model we propose a multi-agent context-aware u-learning system that offers several functionalities such as context-aware learning planning, personalized course evaluation, selection of learning objects according to student profile, search of learning objects in repository federations, search of thematic learning assistants, and access of current context-aware collaborative learning activities involved. In addition, several context-awareness services are incorporated within the adaptive e-learning system that can be used from mobile devices. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of using this kind of approaches in virtual learning environments which constitutes an attempt to improve learning processes.

Keywords

Ubiquitous MAS Adaptive and personalized virtual courses Context-awareness services Mobile devices 

Notes

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” from the Universidad Nacional de Colombia, with code 1119-569-34172. This research was also developed with the aid of the master grant offered to Oscar M. Salazar by COLCIENCIAS through “Convocatoria 617 de 2013. Capítulo 1 Semilleros-Jóvenes Investigadores”.

References

  1. 1.
    Carrillo, A.: Agentes ubicuos para un acceso adaptado de usuarios nómadas en Sistemas de Información: El framework PUMAS. Tesis de Doctorado en Informática, Université Joseph Fourier, Equipe STEAMER - Laboratoire d’Informatique de Grenoble, Francia (2007)Google Scholar
  2. 2.
    Zervas, P., Gómez, S., Fabregat, R., Sampson, D.: Tools for context-aware learning de-sign and mobile delivery. In: Proceedings of the 11th IEEE International Conference on Advanced Learning Technologies (2011)Google Scholar
  3. 3.
    Wang, S., Wu, C.: Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Syst. Appl. 38, 10831–10838 (2011)CrossRefGoogle Scholar
  4. 4.
    Li, J.Z.: Quality, evaluation and recommendation for learning object. In: International Conference on Educational and Information Technology, (ICEIT), pp. 533–537 (2010)Google Scholar
  5. 5.
    Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Boston (2007). ISBN 0-262-23203-0Google Scholar
  6. 6.
    Casali, A., Gerling, V., Deco, C., Bender, C.: Sistema inteligente para la recomendación de objetos de aprendizaje. Generacion Digit. J. 9(1), 88–95 (2011)Google Scholar
  7. 7.
    Rodriguez, P., Salazar, O., Ovalle, D., Duque, N., Moreno, J.: Using ontological modeling for multi-agent recommendation of learning objects. In: Workshop MASLE -Multiagent System Based Learning Environments, Intelligent Tutoring Systems (ITS) Conference, Hawaii, 2–9 June 2014Google Scholar
  8. 8.
    Learning Technology Standards Committee: IEEE Standard for Learning Object Metadata. Institute of Electrical and Electronics Engineering, New York (2002)Google Scholar
  9. 9.
    Rodríguez, P.A., Moreno, J., Duque, N.D., Ovalle, D., Silveira, R.: (Un modelo para la composición semiautomática de contenido educativo desde repositorios abiertos de objetos de aprendizaje) A model for the semi-automatic composition of educational content from open repositories of learning objects. Rev. Electrónica Investig. Educ. (REDIE) 16, 123–136 (2014)Google Scholar
  10. 10.
    Van de Sompel, H., Chute, R., Hochstenbach, P.: The aDORe federation architecture: digital repositories at scale. Int. J. Digit. Libr. 9, 83–100 (2008)CrossRefGoogle Scholar
  11. 11.
    Duque, N.: Modelo adaptativo multi-agente para la planificación y ejecución de cursos virtuales personalizados - Tesis Doctoral, Universidad Nacional de Colombia (2009)Google Scholar
  12. 12.
    Hwang, G., Yang, T., Tsai, C., Yang, S.: A context-aware ubiquitous learning environment for conducting complex science experiments. Comput. Educ. J. 53, 402–413 (2009)CrossRefGoogle Scholar
  13. 13.
    Bellifemine, F., Rimessa, G., Trucco, T., Caire, G.: JADE (Java Agent Development Framework) Programmer’s Guide (2005)Google Scholar
  14. 14.
    Jiménez, M., Ovalle, D., Jiménez, J.: Evaluación en línea para cursos tutoriales inteligentes adaptativos usando el modelo de sistemas multi-agente. Revista Avances en Sistemas e Informática, Universidad Nacional de Colombia. 5(1), 20–29 (2008). ISSN 1657-7663Google Scholar
  15. 15.
    Arias, F.: Multi-agent adaptive model for adaptive virtual courses planning and LO selection. Computer Science MSc Dissertation (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Oscar M. Salazar
    • 1
  • Demetrio A. Ovalle
    • 1
    Email author
  • Néstor D. Duque
    • 2
  1. 1.Universidad Nacional de ColombiaSede MedellínColombia
  2. 2.Universidad Nacional de ColombiaSede ManizalesColombia

Personalised recommendations