A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories

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


Educational Recommender Systems aim to provide students with search relevant results adapted to their needs or preferences and delivering those educational contents such as Learning Objects (LOs) that could be closer than expected. LOs can be defined as a digital entity involving educational design characteristics. Each LO can be used, reused, or referenced during computer-supported learning processes, aiming at generating knowledge, skills, attitudes, and competences based on the student profile. The aim of this paper is to present a student-centered LO recommender system based on a hybrid recommendation technique that combines three following approaches: content-based, collaborative and knowledge-based. In addition, those LOs adapted to the student profile are retrieved from LO repositories using the stored descriptive metadata of these objects. A testing phase with a case study is performed in order to validate the proposed hybrid recommender system that demonstrates the effectiveness of using this kind of approaches in virtual learning environments.


Student-centered hybrid recommender systems Learning objects Metadata Repositories 



The research work 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. It was also developed with the aid of the doctoral grant offered to Paula A. Rodríguez by “Programa Nacional de Formación de Investigadores – COLCIENCIAS, Colombia”.


  1. 1.
    Chesani, F.: Recommendation Systems. Corso di laurea Ing. Inform. 1–32 (2007)Google Scholar
  2. 2.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  3. 3.
    Burke, R.: Hybrid web recommender systems. Adapt. Web 4321, 377–408 (2007)CrossRefGoogle Scholar
  4. 4.
    Li, J.Z.: Quality, evaluation and recommendation for learning object. In: International Conference on Educational and Information Technology, pp. 533–537 (2010)Google Scholar
  5. 5.
    Rodríguez, P.A., Salazar, O., Duque, N.D., Ovalle, D., 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, USA (2014)Google Scholar
  6. 6.
    Learning Technology Standards Committee: IEEE Standard for Learning Object Metadata. Institute of Electrical and Electronics Engineers, New York (2002)Google Scholar
  7. 7.
    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 (2014)Google Scholar
  8. 8.
    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
  9. 9.
    Mizhquero, K., Barrera, J.: Análisis, Diseño e Implementación de un Sistema Adaptivo de Recomendación de Información Basado en Mashups. Rev. Tecnológica ESPOL-RTE (2009)Google Scholar
  10. 10.
    Vekariya, V., Kulkarni, G.R.: 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 (2012)Google Scholar
  11. 11.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-adapt. Interact. 12, 331–370 (2002)CrossRefzbMATHGoogle Scholar
  12. 12.
    Cazella, S.C., Reategui, E.B., Nunes, M.A.: A Ciência da Opinião: Estado da arte em Sistemas de Recomendação. JAI Jorn. Atualização em Informática da SBC. Rio Janeiro, RJ PUC Rio 161–216 (2010)Google Scholar
  13. 13.
    Casali, A., Gerling, V., Deco, C., Bender, C.: Sistema Inteligente para la Recomendación de Objetos de Aprendizaje. Rev. Generación Digit. 9, 88–95 (2011)Google Scholar
  14. 14.
    Alonso, C., Gallego, D., Honey, P.: Los Estilos de Aprendizaje. Procedimientos de diagnostico y mejora, Bilbao (1997)Google Scholar
  15. 15.
    Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-Learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56, 885–899 (2011)CrossRefGoogle Scholar
  16. 16.
    Duque, N.: Modelo Adaptativo Multi-Agente para la Planificación y Ejecución de Cursos Virtuales Personalizados - Tesis Doctoral. Universidad Nacional de Colombia. (2009)Google Scholar
  17. 17.
    Salehi, M., Pourzaferani, M., Razavi, S.A.: Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egypt. Informatics J. 14(1), 67–68 (2013)CrossRefGoogle Scholar
  18. 18.
    Zapata, A., Menendez, V., Prieto, M., Romero, C.: A hybrid recommender method for learning objects. Des. Eval. Digit. Content Educ. Proc. Publ. Int. J. Comput. Appl. 1–7 (2011)Google Scholar
  19. 19.
    Sikka, R., Dhankhar, A., Rana, C.: A survey paper on E-learning recommender system. Int. J. Comput. Appl. 47, 27–30 (2012)Google Scholar
  20. 20.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search. Addison-Wesley, Boston (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paula A. Rodríguez
    • 1
  • Demetrio A. Ovalle
    • 1
    Email author
  • Néstor D. Duque
    • 1
  1. 1.Universidad Nacional de ColombiaBogotáColombia

Personalised recommendations