Adaptation Decisions and Profiles Exchange among Open Learning Management Systems Based on Agent Negotiations and Machine Learning Techniques

  • Silvia Baldiris
  • Ramón Fabregat
  • Carolina Mejía
  • Sergio Gómez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5613)

Abstract

We have developed some projects [1,2] for addressing the heterogeneity problem in open learning management systems (LMS). In [3], an independent adaptation platform to support competences development through personalization is presented. Three user characteristics (competences profile, learning style, and accessing context) are modeled by means of analyzing user interaction data in a LMS. This process is supported by the assigment of independent adaptation tasks to different JADE intelligent agents. In this paper we introduce some negotiation strategies among those intelligent agents in order to: 1) select the best types of adaptation through collaborative tasks, and 2) generate standards and exchangeable user profiles based on the inferred user characteristics, describing the mechanisms to mobilize these profiles between different LMSs. These profiles support the generation of specifics learning designs for each particular user.

Keywords

competence development adaptation intelligent agents adaptive hypermedia machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Peña, C.I.: PhD Thesis: Intelligent agents to improve adaptivity in a web-based learning environment. Universidad de Girona (2004)Google Scholar
  2. 2.
    Mérida, D., Cannataro, M., Fabregat, R., Arteaga, C.: MAS-SHAAD a Multiagent System Proposal for an Adaptive Hypermedia System. In: Proceedings of IJCEELL journal Special issue: Adaptivity in Web and Mobile Learning Services (2004)Google Scholar
  3. 3.
    Baldiris, S., Santos, O., Huerva, D., Fabregat, R., Boticario, J.G.: Multidimensional Adaptations for Open Learning Management Systems. Accepted at TUMASA Workshop of 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney (Australia), December 9-12 (2008)Google Scholar
  4. 4.
    Baldiris, S., Santos, O.C., Barrera, C., Boticario, J.G., Velez, J., Fabregat, R.: Integration of educational specifications and standards to support adaptive learning scenarios in ADAPTAPlan. International Journal of Computer Science and Applications (IJCSA). Special Issue on New Trends on AI techniques for Educational Technologies 5, 1 (2008)Google Scholar
  5. 5.
    Moreno, G.D., Baldiris, S.M.: Degree project memories: Adaptive Hypermedia System for Teaching Object Oriented Programming. Universidad Industrial de Santander (2003)Google Scholar
  6. 6.
    Santos, O.C., Boticario, J.G.: Meaningful pedagogy via covering the entire life cycle of adaptive eLearning in terms of a pervasive use of educational standards: the aLFanet experience. In: Nejdl, W., Tochtermann, K. (eds.) EC-TEL 2006. LNCS, vol. 4227, pp. 691–696. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Shasha, D., Zhang, K.: Fast algorithms for the unit cost editing distance between trees. Journal of Algorithms 11, 581–621 (1990)CrossRefMATHGoogle Scholar
  8. 8.
    Buttler, D.: A Short Survey of Document Structure Similarity Algorithms. In: International Conference on Internet Computing 2004, pp. 3–9 (2004)Google Scholar
  9. 9.
    Flesca, S., Manco, G., Masciari, E., Pontieri, L., Pugliese, A.: Fast Detection of XML Structural Similarity. IEEE Transaction on Knowledge and Data Engineering 17(2), 160–175 (2005)CrossRefGoogle Scholar
  10. 10.
    Chawathe, S., Garcia-Molina, H.: Meaningful change detection in structured data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, USA, May 13-15 (1997)Google Scholar
  11. 11.
    Chawathe, S., Rajaraman, A., Garcia-Molina, H., Widom, J.: Change detection in hierarchically structured information. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6 (1996)Google Scholar
  12. 12.
    Cubera, F., Epstein, D.: Fast Difference and Update of XML Documents, March 1999. Xtech, San Jose (1999)Google Scholar
  13. 13.
    Cobena, G., Abiteboul, S., Marian, A.: Detecting changes in XML documents. In: Proceedings of the 18th International Conference on Data Engineering, San Jose, California, USA, February 26 - March 1 (2002)Google Scholar
  14. 14.
    Wang, Y., DeWitt, D., Cai, J.: X-Diff: An effective change detection algorithm for XML documents. In: Proceedings of the 19th International Conference on Data Engineering, Bangalore, India, March 5-8 (2003)Google Scholar
  15. 15.
    Brusilovsky, P., Millán, E.: User Models for Adaptive Hypermedia and Adaptive Educational Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Van Der Sluijs, K., Houben, G.: A generic component for exchanging user models between web-based systems. International Journal of Continuing Engineering Education and Life-Long Learning 16(1/2), 64–76 (2006)CrossRefGoogle Scholar
  17. 17.
    Kay, J.: The um Toolkit for reusable, long term user models. User Modeling and User-Adapted Interaction 4(3), 149–196 (1995)CrossRefGoogle Scholar
  18. 18.
    Kobsa, A.: Modeling the user’s conceptual knowledge in BGP-MS, a user modeling shell system. Comput. Intelligence 6, 193–208 (1990)CrossRefGoogle Scholar
  19. 19.
    Orwant, J.: Heterogenous learning in the Doppelgänger user modeling system. User Model. User-Adapted Interact. J. Personal. Res. 4(2), 107–130 (1995)CrossRefGoogle Scholar
  20. 20.
    Paiva, A., Self, J.: TAGUS: A user and learner modeling workbench. User Model. User-Adapted Interact. J. Personal. Res. 4(3), 197–226 (1995)Google Scholar
  21. 21.
    Finin, T.W., Drager, D.: GUMS1: A general user modeling system. In: Sixth Canadian Conference on Artificial Intelligence, Montreal, Canada, pp. 24–29 (1986) Google Scholar
  22. 22.
    Vergara, H.: PROTUM: a prolog based tool for user modeling. WIS-Report 10, WG Knowledge-Based Information Systems, Department of Information Science, University of Konstanz, Germany (1994)Google Scholar
  23. 23.
    Brajnik, G., Tasso, C.: A shell for developing non-monotonic user modeling systems. Int. J. Human-Computer Studies 40, 31–62 (1994)CrossRefGoogle Scholar
  24. 24.
    Al-Ekram, R., Adma, A., Baysal, O.: diffX: An Algorithm to Detect Changes in Multi-Version XML Documents. School of Computer Science, University of Waterloo (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Silvia Baldiris
    • 1
  • Ramón Fabregat
    • 1
  • Carolina Mejía
    • 1
  • Sergio Gómez
    • 1
  1. 1.Institute of Informatics and Aplications (IIiA)Universitat de GironaSpain

Personalised recommendations