BiTutor–A Component Based Architecture for Developing Web Based Intelligent Tutoring System

  • Yaser Nouh
  • Varunkumar Nagarajan
  • R. Nadarajan
  • Maytham Safar
Part of the Advances in Soft Computing book series (AINSC, volume 43)

Abstract

In this paper we present an overview of the architectural design of the BiTutor which is a Bayesian Intelligent Tutoring System. This is an on-going research whose final goal is to build an open Intelligent Tutoring System (ITS). Every component of the architecture has its own strategies and tools that makes it intelligent. This makes the entire system posses adaptive capabilities. The design includes web-based distributed architecture, AI techniques used and programmer-optimized user interface. It is hypothesized that the completed prototype will be sufficient to prove the concept. A fully developed BiTutor will provide an interactively-rich learning environment for students that will result in increased achievement.

Keywords

Intelligent Tutoring System Web Adaptive 

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References

  1. 1.
    Brusilovsky, P.: Adaptive and Intelligent Technologies for Web based Education. Special Issue on Intelligent System and TeleTeaching 4, 19–25 (1999)Google Scholar
  2. 2.
    Carmona, C., et al.: Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 347–356. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Conati, C., Gertner, A., VanLehn, K.: Using Bayesian Networks to Manage Uncertainty in Student Modeling. User Modeling and User-Adapted Interaction 12(4), 371–417 (2002)MATHCrossRefGoogle Scholar
  4. 4.
    Henze, N., Nedjl, W.: Student Modeling for the KBS Hyperbook System using Bayesian Networks. Technical report, University of Hannover (November 1998)Google Scholar
  5. 5.
    Horvitz, E., et al.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proceedings of UAI‘98, pp. 256–265. Morgan Kaufmann, San Francisco (1998)Google Scholar
  6. 6.
    Tchétagni, J.M.P., Nkambou, R.: Hierarchical Representation and Evaluation of the Student in an Intelligent Tutoring System. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Mayo, E., Mitrovic, A.: Using Probabilistic Student Model to Control Problem Difficulty. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 525–533. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Weber, G., Brusilovsky, P.: ELM -ART: An adaptive Versatile System for Web-based Instruction. International Journal of Artificial Intelligence in Education, Special Issue on Adaptive and Intelligent Web-based Educational Systems 12(4), 351–384 (2001)Google Scholar
  9. 9.
    Yao, J.T., Yao, Y.Y.: Web-based support system. In: Proceedings of Second Indian International Conference on Web Intelligence, Canada (2003)Google Scholar
  10. 10.
    Yaser, N., et al.: Bayesian student Modeling in a Distributed Environment. In: First International Conference on Digital Communications and Computer Applications, DCCA-2007, Jordan, pp. 1132–1140 (2007)Google Scholar
  11. 11.
    Yaser, N., Karthikeyani, P., Nadarajan, R.: Intelligent Tutoring System - Bayesian Student Model. In: Proceedings of First IEEE International Conference on Digital Information Management, IDCIM-2006, Bangalore, India, pp. 257–262. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yaser Nouh
    • 1
  • Varunkumar Nagarajan
    • 1
  • R. Nadarajan
    • 1
  • Maytham Safar
    • 2
  1. 1.Mathematics and Computer Applications Department, PSG College of Technology, Coimbatore 641004India
  2. 2.Computer Engineering Department, Kuwait UniversityKuwait

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