Advertisement

Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System

  • Patrick Person
  • Thierry Galinho
  • Hadhoum Boukachour
  • Florence Lecroq
  • Jean GrieuEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Abstract

In this paper we present an intelligent tutoring system which aims at decreasing students’ dropout rate by offering the possibility of a personalized follow-up. We address the specific problem of the evolution of the large amount of data to be processed and interpreted in an intelligent tutoring system. In this regard we detail the architecture and experimental results of our decision support system used as the core of the intelligent tutor—which could be applied to a variety of teaching fields. The first part presents an overview of the characteristics of intelligent tutors, the chosen data organization—composed of a composite factual semantic feature descriptive representation associated to a multiagent system—and two examples used to illustrate the architecture of our prototype. The second and last part describes all the components of the prototype: student interface, dynamic representation layer, characterization, and interpretation layers. First, for the student interface, the system shows our 3D virtual campus named GE3D to be connected to the intelligent tutor. Then we explain how the agents of the first layer represent the evolution of the situation being analyzed. Next, we specify the use of the characterization layer to cluster the agents of representation layer and to compute compound parameters. Finally, we expose how—using compound parameters—the third layer can measure similarity between current target case and past cases to constitute an interpretation of cases according to a case-based reasoning paradigm.

Keywords

Decision Support System Multiagent System Cardinal Number Programmable Logic Controller Intelligent Tutoring System 
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.

References

  1. 1.
    Aamodt, A.: Case-based reasoning and intelligent tutoring. In: SAIS-SSLS Proceedings, pp. 8–22. Vasteras, Sweden (2005)Google Scholar
  2. 2.
    Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-agent Systems with JADE. Wiley (2007)Google Scholar
  3. 3.
    Clemens, M.: The art of complex problem solving. http://www.idiagram.com/CP/cpprocess.html
  4. 4.
    Craw, S.: Case based reasoning lecture. Robert Gordon University (2013). http://www.comp.rgu.ac.uk/staff/smc/teaching/cm3016/Lecture-1-cbr-intro.ppt
  5. 5.
    Denning, P.: Computer science: the discipline. In: Ralston, A., Hemmendinger, D. (eds.) Encyclopedia of Computer Science. Wiley (2000)Google Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231. AAAI Press (1996)Google Scholar
  7. 7.
    Grieu, J., Lecroq, F., Person, P., Galinho, T., Boukachour, H.: GE3D: a virtual campus for technology-enhanced distance learning. Int. J. Emerg. Technol. Learn. 5(3), 12–17 (2010). doi: 10.3991/ijet.v5i3.1388. http://online-journals.org/i-jet/article/view/1388
  8. 8.
    Hafner, K.: Software tutors offer help and customized hints (2004). http://www.nytimes.com/2004/09/16/technology/circuits/16tuto.html?_r=0
  9. 9.
    Knuth, D.E.: The Art of Computer Programming, Volume I: Fundamental Algorithms, 3rd edn. Addison-Wesley (1997)Google Scholar
  10. 10.
    Kolodner, J.: An introduction to case-based reasoning. Artif. Intell. Rev. 6, 3–34 (1992)CrossRefGoogle Scholar
  11. 11.
    Lecroq, F., Grieu, J., Person, P., Galinho, T., Boukachour, H.: Intelligent tutoring system in GE3D virtual campus. Int. J. Comput. Sci. Artif. Intell. 6 (2012). http://www.jcsai.org/paperInfo.aspx?ID=20
  12. 12.
    Luger, G.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison-Wesley (2005)Google Scholar
  13. 13.
    Lyne, O.: Risk faq—version 5.61. http://www.kent.ac.uk/smsas/personal/odl/riskfaq.htm
  14. 14.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability pp. 281–297 (1967)Google Scholar
  15. 15.
    Murr, M.: How forensic tools recover digital evidence (data structures) (2007). http://www.forensicblog.org/how-forensic-tools-recover-digital-evidence-data-structures
  16. 16.
    Paviotti, G., Rossi, P., Zarka, D.: Intelligent tutoring systems: an overview (2012). http://www.intelligent-tutor.eu/files/2012/06/2012_Intelligent_Tutoring_Systems_overview.pdf
  17. 17.
    Person, P., Galinho, T., Lecroq, F., Boukachour, H., Grieu, J.: Intelligent tutor design for a 3D virtual campus. In: IEEE Conference of Intelligent Systems (IS12), pp. 74–79. Sofia, Bulgaria (2012). http://dx.doi.org/10.1109/IS.2012.6335194
  18. 18.
  19. 19.
    Wenger, E.: Artificial Intelligence and Tutoring Systems: Computational Approaches to the Communication of Knowledge. Morgan Kaufmann (1987)Google Scholar
  20. 20.
    Willging, S.: Factors that influence students’ decision to dropout of online courses. J. Asynchronous Learn. Networks 8(4), 105–118 (2004)Google Scholar
  21. 21.
    Wolf, M.: An intelligent artificial player for the game of Risk. Master’s thesis, TU Darmstadt, Knowledge Engineering Group (2005). http://www.ke.informatik.tu-darmstadt.de/lehre/arbeiten/diplom/2005/Wolf_Michael.pdf. Diplom
  22. 22.
    Wooldridge, M.J.: An Introduction to MultiAgent Systems, 2nd edn. Wiley (2009)Google Scholar
  23. 23.
    Woolf, B.: Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning. Elsevier Science (2010)Google Scholar
  24. 24.
    Zouhair, A., En-Naimi, E.M., Amami, B., Boukachour, H., Person, P., Bertelle, C.: Multiagent case-based reasoning and individualized follow-up of learner in remote learning. In: The 2nd International Conference on Multimedia Computing and Systems, (ICMCS’11), pp. 1–6. Ouarzazate , Morocco (2011).  10.1109/ICMCS.2011.5945644
  25. 25.
    Zouhair, A.: Raisonnement à Partir de cas dynamique multi-agents : application à un système de tuteur intelligent. PhD thesis, Tanger, Morocco (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Patrick Person
    • 1
  • Thierry Galinho
    • 1
  • Hadhoum Boukachour
    • 1
  • Florence Lecroq
    • 1
  • Jean Grieu
    • 1
    Email author
  1. 1.LITIS University of Le HavreLe HavreFrance

Personalised recommendations