A Multi-Criteria Programming Model for Intelligent Tutoring Planning

  • Ruihong Shi
  • Peng Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


This paper proposed a practical approach to personalized tutoring planning by exploiting existing tutoring resources (e.g., a book, a courseware). More exactly, it does not build an instructional course from scratch – from the domain curriculum, as most Intelligent Tutoring Systems (ITS) do. Instead, information in the curriculum model is used as metadata, together with other metadata, to describe tutoring resources. Given a learning requirement (learning objectives and/or constraints), it finds out the most appropriate tutoring resource(s) and proposes a proper learning sequence of them. Based on the proposed fuzzy instructional model and learner model, the tutoring planning problem is defined as a multi-criteria programming (MCP) model.


Learning Object User Model Learning Plan Learning Topic Mastery Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Mühlenbrock, M., Tewissen, F., Hoppe, H.U.: A Framework System for Intelligent Support in Open Distributed Learning Environments. Int. J. Artificial Intelligence in Education, The International Society for Artificial Intelligence in Education 9, 256–274 (1998)Google Scholar
  2. 2.
    Rodrigues, M., Novais, P., Santos, M.F.: Future Challenges in Intelligent Tutoring Systems - A Framework. Recent Research Developments in Learning Technologies. In: Proc. 3rd Int. Conf. Multimedia and Information & Communication Technologies in Education, pp. 129–134 (2005)Google Scholar
  3. 3.
    Rosić, M., Glavinić, V., Stankov, S.: Distance Learning System based on Distributed Semantic Networks. In: EUROCON 2003, Computer as a Tool, Region 8, vol. 2, pp. 26–29. IEEE, Los Alamitos (2003)CrossRefGoogle Scholar
  4. 4.
    MYMIC LLC: Outstanding research issues in Intelligent tutoring systems. scientific and technical report (2004-4-21) Google Scholar
  5. 5.
    Knolmayer, G.F.: Decision support models for composing and navigating through e-learning objects. In: Proc. 36th Hawaii Int. Conf. System Sciences, IEEE, Los Alamitos (2003)Google Scholar
  6. 6.
    Nkambou, R.: Using fuzzy logic in ITS-course generation. In: Proc. 9th Int. Conf. Tools with Artificial Intelligence, pp. 190–193. IEEE, Los Alamitos (1997)CrossRefGoogle Scholar
  7. 7.
    Xu, D.M., Wang, H.Q., Su, K.L.: Intelligent student profiling with fuzzy models. In: Proc. 35th Hawaii Int. Conf. System Sciences, IEEE, Los Alamitos (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruihong Shi
    • 1
    • 2
  • Peng Lu
    • 3
  1. 1.Key Laboratory of Intelligent Information ProcessingInstitute of Computing Technology, Chinese Academy of Sciences 
  2. 2.Graduate School of the Chinese Academy of Sciences 
  3. 3.College of Computer Science and TechnologyBeijing Institute of Technology 

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