Intelligent Recovery Architecture for Personalized Educational Content

  • A. GilEmail author
  • S. Rodríguez
  • F. De la Prieta
  • B. Martín
  • M. Moreno
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 156)


Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This work proposes a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents. The model allows the development of an open and flexible architecture that supports the services necessary to conduct a search for distributed digital content dynamically. AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is based on the proposed model; it is a multi-agent architecture that can search and integrate heterogeneous educational content through a recovery model that uses a federated search. A major challenge in searching and retrieval digital content is to efficiently find the most suitable for the users This paper proposes a new approach to filter the educational content retrieved based on Case-Based Reasoning (CBR).The model and the technologies presented in this research are an example of the potential for developing recovery systems for digital content based on the paradigm of virtual organizations of agents. The advantages of the proposed architecture are its flexibility, customization, integrative solution and efficiency.


Multi-agent systems e-learning learning objects repositories Case Base Reasoning recommender systems 


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  1. 1.
    Bobadilla, J., Serradilla, F., Hernando, A., MovieLens: Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems (2009), doi:10.1016/j.knosys.2009.01.008Google Scholar
  2. 2.
    Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educational Technology & Society 12(4), 30–42 (2009)Google Scholar
  3. 3.
    Manouselis, N., Vuorikari, R., Van Assche, F.: Collaborative recommendation of e-learning resources: an experimental investigation. Journal of Computer Assisted Learning 26, 227–242 (2010)CrossRefGoogle Scholar
  4. 4.
    Recker, M., Walker, A., Lawless, K.: What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education. Instructional Science 31(4-5), 299–316 (2003)CrossRefGoogle Scholar
  5. 5.
    Lemire, D., Boley, H., McGrath, S., Ball, M.: Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation. Technolodgy and Smart Education 2(3), 179–188 (2005)CrossRefGoogle Scholar
  6. 6.
    McCalla, G.: The Ecological Approach to the Design of E-Learning Environments: Purpose-based Capture and Use of Information about Learners. Journal of Interactive Media in Education, Special Issue on the Educational Semantic Web 1(7), 18 (2004)Google Scholar
  7. 7.
    Manouselis, N., Vuorikari, R., Van Assche, F.: Simulated Analysis of Collaborative Filtering for Learning Object Recommendation. In: SIRTEL Workshop, EC-TEL (2007)Google Scholar
  8. 8.
    Aijuan, D., Baoying, W.: Domain-based recommendation and retrieval of relevant materials in e-learning. In: IEEE International Workshop on Semantic Computing and Applications 2008 (IWSCA 2008), pp. 103–108 (2008)Google Scholar
  9. 9.
    Ghauth, K., Abdullah, N.: Learning materials recommendation using good learners’ ratings and content-based filtering. In: Educational Technology Research and Development. Springer, Boston (2010),, ISSN 1042-1629
  10. 10.
    Wang, T.I., Tsai, K.H., Lee, M.C., Chiu, T.K.: Personalized Learning Objects Recommendation based on the Semantic Aware Discovery and the Learner Preference Pattern. Educational Technology and Society 10(3), 84–105 (2007)Google Scholar
  11. 11.
    Ochoa, X., Duval, E.: Use of Contextualized Attention Metadata for Ranking and Recommending Learning Objects. In: Proceedings of 1st International Workshop on Contextualized Attention Metadata: Collecting, Managing and Exploiting of Rich Usage Information, pp. 9–16 (2006)Google Scholar
  12. 12.
    Wolpers, M., Najjar, J., Duval, E.: Tracking Actual Usage: the Attention Metadata Approach. Educational Technology & Society 10(3), 106–121 (2007)Google Scholar
  13. 13.
    Han, Q., Gao, F., Wang, H.: Ontology-based learning object recommendation for cognitive considerations. In: 8th World Congress on Intelligent Control and Automation (WCICA), July 7-9, pp. 2746–2750 (2010)Google Scholar
  14. 14.
    Ruiz-Iniesta, A., Jiménez-Díaz, G., Gómez-Albarrán, M.: Personalización en Recomendadores Basados en Contenido y su Aplicación a Repositorios de Objetos de Aprendizaje. IEEE-RITA 5(1), 31–38 (2010)Google Scholar
  15. 15.
    Vargo, J., Nesbit, J.C., Belfer, K., Archambault, A.: Learning object evaluation: Computer mediated collaboration and inter-rater reliability. International Journal of Computers and Applications 25(3), 198–205 (2003)Google Scholar
  16. 16.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender Systems in Technology Enhanced Learning. In: Recommender Systems Handbook, pp. 387–415. Springer (2011)Google Scholar
  17. 17.
    Montaner, M., López, B., de la Rosa, J.L.: Opinion-Based Filtering Through Trust. In: Klusch, M., Ossowski, S., Shehory, O. (eds.) CIA 2002. LNCS (LNAI), vol. 2446, pp. 164–178. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • A. Gil
    • 1
    Email author
  • S. Rodríguez
    • 1
  • F. De la Prieta
    • 1
  • B. Martín
    • 1
  • M. Moreno
    • 1
  1. 1.Departamento de Informática y Automática – Facultad de CienciasUniversity of SalamancaSalamancaSpain

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