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CBR Proposal for Personalizing Educational Content

  • Ana GilEmail author
  • Sara Rodríguez
  • Fernando De la Prieta
  • Juan F. De Paz
  • Beatriz Martín
Conference paper
  • 539 Downloads
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 152)

Abstract

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). AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is a multi-agent architecture that can search and integrate heterogeneous educational content within the CBR model proposes. The recommendation model and the technologies reported in this research applied to educational content are an example of the potential for personalizing labeled educational content recovered from heterogeneous environments.

Keywords

E-learning learning objects Case Base Reasoning recommender systems Multi-agent systems 

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References

  1. 1.
    Gil, A.B., De la Prieta, F., Rodríguez, S.: Automatic Learning Object Extraction and Classification in Heterogeneous Environments. In: Pérez, J.B., Corchado, J.M., Moreno, M.N., Julián, V., Mathieu, P., Canada-Bago, J., Ortega, A., Caballero, A.F. (eds.) Highlights in Practical Applications of Agents and Multiagent Systems. AISC, vol. 89, pp. 109–116. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Chiappe, A., Segovia, Y., Rincon, H.Y.: Toward an instructional design model based on learning objects. Educational Technology Research and Development 55, 671–681 (2007)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Bobadilla, J., Serradilla, F., Hernando, A., Lens, M.: Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems (2009), doi:10.1016/j.knosys.2009.01.008Google Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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 (7) (2004); Special Issue on the Educational Semantic Web 1, 18 (2004)Google Scholar
  10. 10.
    Manouselis, N., Vuorikari, R., Van Assche, F.: Simulated Analysis of Collaborative Filtering for Learning Object Recommendation. In: SIRTEL Workshop, EC-TEL (2007)Google Scholar
  11. 11.
    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
  12. 12.
    Ghauth, K., Abdullah, N.: Learning materials recommendation using good learners’ ratings and content-based filtering. In: Educational Technology Research and Development, SN 1042–1629. Springer, Boston (2010), http://dx.doi.org/10.1007/s11423-010-9155-4 Google Scholar
  13. 13.
    Tsai, K.H., Chiu, T.K., Lee, M.C., Wang, T.I.: A learning Object Recommendation Model based on the Preference and Ontological Approaches. In: Proceeding of the Sixth International Conference on Advanced Learning Technologies, ICALT 2006 (2006)Google Scholar
  14. 14.
    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
  15. 15.
    Yang, Y.: An evaluation of statistical approaches to text categorization. Journal of Information Retrieval 1, 67–88 (1999)CrossRefGoogle Scholar
  16. 16.
    Kerkiri, T., Manitsaris, A., Mavridou, A.: Reputation metadata for recommending personalized e-learning resources. In: Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization, Uxbridge, pp. 110–115 (2007)Google Scholar
  17. 17.
    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
  18. 18.
    Wolpers, M., Najjar, J., Duval, E.: Tracking Actual Usage: the Attention Metadata Approach. Educational Technology & Society 10(3), 106–121 (2007)Google Scholar
  19. 19.
    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
  20. 20.
    Ruiz-Iniesta, A., Jiménez-Díaz, G., Gómez-Albarrán, M.: Personalización en Recomendadores Basados en Contenido y su Aplicación a Repositorios de Objetos de Aprendizaje. In: IEEE-RITA, vol. 5(1), pp. 31–38 (2010)Google Scholar
  21. 21.
    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
  22. 22.
    Corchado, J.M., Laza, R.: Constructing Deliberative Agents with Case-based Reasoning Technology. International Journal of Intelligent Systems 18(12), 1227–1241 (2003)CrossRefGoogle Scholar
  23. 23.
    Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical Model for Constructing Deliberative Agents. Engineering Intelligent Systems 3, 173–185 (2002)Google Scholar
  24. 24.
    Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy CoRRcmp-lg/9709008 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ana Gil
    • 1
    Email author
  • Sara Rodríguez
    • 1
  • Fernando De la Prieta
    • 1
  • Juan F. De Paz
    • 1
  • Beatriz Martín
    • 1
  1. 1.Departamento de Informática y Automática - Facultad de CienciasUniversity of SalamancaSalamancaSpain

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