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User Modeling in the Context of Cognitive Service Delivery: Application to Learning Management Systems

  • Y. Kritikou
  • P. Demestichas
  • E. Adamopoulou
  • K. Demestichas

Abstract

—A contemporary trend in the field of telecommunications is the development of a constantly increasing number of services available to users through computer networks. These services are being used in order to facilitate users’ everyday life and save them time and effort. The following paper discusses on the service delivery and the way it can be adapted to each user’s specific needs, in the context of cognitive networks and service provisioning. An example of such a service is being examined, namely a Learning Management System and specifically User Model entity, which is responsible for storing user’s preferences. In support of this vision, a paradigm of Bayesian Networks’ application is presented, aiming at predicting user’s preferences in a Learning Management System, by managing a specific set of parameters that affect it and providing the information to configure the learning content to be delivered, accordingly. For the confirmation of this Model’s validity a set of indicative results are also presented at the end of this paper. Index Terms‐E‐learning, Learning Management System, Service Provisioning, User model

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References

  1. [1]
    J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kauffman, 1988.Google Scholar
  2. [2]
    V. Terziyan, “A Bayesian Metanetwork”, International Journal on Artificial Intelligence Tools, vol. 14, 2004.Google Scholar
  3. [3]
    D. Heckerman, “A Tutorial on Learning with Bayesian Networks”, Technical Report MSR-TR-95-06, Microsoft Research, March 1995.Google Scholar
  4. [4]
    E. Homayounvala, S. A. Ghorashi, A. H. Aghvami, “A Bayesian approach to modelling user preferences for reconfiguration”, in: E2R Workshop on Reconfigurable Mobile Systems and Networks Beyond 3G, Barcelona, Spain, September 2004.Google Scholar
  5. [5]
    J. Cheng, R. Greiner, “Learning Bayesian belief network classifiers: algorithms and system”, Proc. 14th Canadian Conference on Artificial Intelligence, pp. 141-151, 2001.Google Scholar
  6. [6]
    E. Adamopoulou, K. Demestichas, A. Koutsorodi, and M. E. Theologou, “Access Selection and User Profiling in Reconfigurable Terminals”, in Proc. of the 15th Wireless World Research Forum (WWRF), Paris, Dec. 2005.Google Scholar
  7. [7]
    J. Cheng and R. Greiner, “Learning Bayesian belief network classifiers: algorithms and system”, in roc. of the 14th Canadian Conference on Artificial Intelligence, pp. 141-151, 2001.Google Scholar
  8. [8]
    http://en.wikipedia.org/wiki/E-learningGoogle Scholar
  9. [9]
    D. Boscovic, Cognitive Networks, http://www.motorola.com/mot/doc/6/6005_MotDoc.pdfGoogle Scholar
  10. [10]
    http://en.wikipedia.org/wiki/Main_PageGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Y. Kritikou
    • 1
  • P. Demestichas
    • 1
  • E. Adamopoulou
    • 2
  • K. Demestichas
    • 2
  1. 1.Department of Digital SystemsUniversity of PiraeusGreece
  2. 2.Department of Electrical and Computer EngineeringNational and Technical University of AthensGreece

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