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Modeling a Teacher in a Tutorial-like System Using Learning Automata

  • B. John Oommen
  • M. Khaled Hashem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7430)

Abstract

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial-like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students.

In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately.

To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students.

Keywords

Tutorial-like Systems Learning Automata Modeling of Adaptive Tutorial Systems Modeling of Teacher 

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References

  1. 1.
    Hashem, M.K.: Learning Automata Based Intelligent Tutorial-like Systems. PhD thesis, School of Computer Science, Carleton University, Ottawa, Canada (2007)Google Scholar
  2. 2.
    Agache, M., Oommen, B.J.: Generalized pursuit learning schemes: New families of continuous and discretized learning automata. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 32(6), 738–749 (2002)CrossRefGoogle Scholar
  3. 3.
    Lakshmivarahan, S.: Learning Algorithms Theory and Applications. Springer (1981)Google Scholar
  4. 4.
    Najim, K., Poznyak, A.S.: Learning Automata: Theory and Applications. Pergamon Press, Oxford (1994)Google Scholar
  5. 5.
    Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Prentice-Hall, New Jersey (1989)Google Scholar
  6. 6.
    Obaidat, M.S., Papadimitrious, G.I., Pomportsis, A.S.: Learning automata: Theory, paradigms, and applications. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 32(6), 706–709 (2002)CrossRefGoogle Scholar
  7. 7.
    Poznyak, A.S., Najim, K.: Learning Automata and Stochastic Optimization. Springer, Berlin (1997)zbMATHGoogle Scholar
  8. 8.
    Thathachar, M.A.L., Sastry, P.S.: Networks of Learning Automata: Techniques for Online Stochastic Optimization. Kluwer Academic, Boston (2003)Google Scholar
  9. 9.
    Tsetlin, M.L.: Automaton Theory and the Modeling of Biological Systems. Academic Press, New York (1973)Google Scholar
  10. 10.
    Thathachar, M.A.L., Sastry, P.S.: Varieties of learning automata: An overview. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 32(6), 711–722 (2002)CrossRefGoogle Scholar
  11. 11.
    Oommen, B.J., Agache, M.: Continuous and discretized pursuit learning schemes: Various algorithms and their comparison. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 31, 277–287 (2001)CrossRefGoogle Scholar
  12. 12.
    Oommen, B.J., Hashem, M.K.: Modeling a Student’s Behavior in a Tutorial-like System Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics SMC-40(B), 481–492 (2010)Google Scholar
  13. 13.
    Oommen, B.J., Hashem, M.K.: Modeling a Domain in a Tutorial-like System Using Learning Automata. Acta Cybernetica 19, 635–653 (2010)zbMATHGoogle Scholar
  14. 14.
    Oommen, B.J., Hashem, M.K.: Modeling a Student-Classroom Interaction in a Tutorial-like System Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics SMC-40(B), pp. 29–42 (2010)Google Scholar
  15. 15.
    Thathachar, M.A.L., Oommen, B.J.: Discretized reward-inaction learning automata. Journal of Cybernetics and Information Science 24–29 (Spring 1979)Google Scholar
  16. 16.
    Misra, S., Oommen, B.J.: GPSPA: A new adaptive algorithm for maintaining shortest path routing trees in stochastic networks. International Journal of Communication Systems 17, 963–984 (2004)CrossRefGoogle Scholar
  17. 17.
    Obaidat, M.S., Papadimitriou, G.I., Pomportsis, A.S., Laskaridis, H.S.: Learning automata-based bus arbitration for shared-medium ATM switches. IEEE Transactions on Systems, Man, and Cybernetics: Part B 32, 815–820 (2002)CrossRefGoogle Scholar
  18. 18.
    Oommen, B.J., Roberts, T.D.: Continuous learning automata solutions to the capacity assignment problem. IEEE Transactions on Computers C-49, 608–620 (2000)Google Scholar
  19. 19.
    Papadimitriou, G.I., Pomportsis, A.S.: Learning-automata-based TDMA protocols for broadcast communication systems with bursty traffic. IEEE Communication Letters, 107–109 (2000)Google Scholar
  20. 20.
    Atlassis, A.F., Loukas, N.H., Vasilakos, A.V.: The use of learning algorithms in atm networks call admission control problem: A methodology. Computer Networks 34, 341–353 (2000)CrossRefGoogle Scholar
  21. 21.
    Atlassis, A.F., Vasilakos, A.V.: The use of reinforcement learning algorithms in traffic control of high speed networks. Advances in Computational Intelligence and Learning, 353–369 (2002)Google Scholar
  22. 22.
    Vasilakos, A., Saltouros, M.P., Atlassis, A.F., Pedrycz, W.: Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques. IEEE Transactions on Systems Science, and Cybernetics, Part C 33, 297–312 (2003)CrossRefGoogle Scholar
  23. 23.
    Seredynski, F.: Distributed scheduling using simple learning machines. European Journal of Operational Research 107, 401–413 (1998)zbMATHCrossRefGoogle Scholar
  24. 24.
    Kabudian, J., Meybodi, M.R., Homayounpour, M.M.: Applying continuous action reinforcement learning automata (CARLA) to global training of hidden markov models. In: Proceedings of the International Conference on Information Technology: Coding and Computing, ITCC 2004, Las Vegas, Nevada, pp. 638–642 (2004)Google Scholar
  25. 25.
    Meybodi, M.R., Beigy, H.: New learning automata based algorithms for adaptation of backpropagation algorithm pararmeters. International Journal of Neural Systems 12, 45–67 (2002)Google Scholar
  26. 26.
    Unsal, C., Kachroo, P., Bay, J.S.: Simulation study of multiple intelligent vehicle control using stochastic learning automata. Transactions of the Society for Computer Simulation International 14, 193–210 (1997)Google Scholar
  27. 27.
    Oommen, B.J., Croix, E.D.S.: Graph partitioning using learning automata. IEEE Transactions on Computers C-45, 195–208 (1995)Google Scholar
  28. 28.
    Collins, J.J., Chow, C.C., Imhoff, T.T.: Aperiodic stochastic resonance in excitable systems. Physical Review E 52, R3321–R3324 (1995)Google Scholar
  29. 29.
    Cook, R.L.: Stochastic sampling in computer graphics. ACM Trans. Graph. 5, 51–72 (1986)CrossRefGoogle Scholar
  30. 30.
    Barzohar, M., Cooper, D.B.: Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 707–722 (1996)CrossRefGoogle Scholar
  31. 31.
    Bertsimas, D.J., Ryzin, G.V.: Stochastic and Dynamic vehicle routing in the euclidean plane with multiple capacitated vehicles. Operations Research 41, 60–76 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Brandeau, M.L., Chiu, S.S.: An overview of representative problems in Location Research. Management Science 35, 645–674 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Bettstetter, C., Hartenstein, H., Pérez-Costa, X.: Stochastic properties of the random waypoint mobility model. Journal Wireless Networks 10, 555–567 (2004)CrossRefGoogle Scholar
  34. 34.
    Rowlingson, B.S., Diggle, P.J.: SPLANCS: spatial point pattern analysis code in S-Plus. University of Lancaster, North West Regional Research Laboratory (1991)Google Scholar
  35. 35.
    Paola, M.: Digital simulation of wind field velocity. Journal of Wind Engineering and Industrial Aerodynamics 74-76, 91–109 (1998)Google Scholar
  36. 36.
    Cusumano, J.P., Kimble, B.W.: A stochastic interrogation method for experimental measurements of global dynamics and basin evolution: Application to a two-well oscillator. Nonlinear Dynamics 8, 213–235 (1995)CrossRefGoogle Scholar
  37. 37.
    Baddeley, A., Turner, R.: Spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software 12, 1–42 (2005)Google Scholar
  38. 38.
    Vasilakos, A.V., Saltouros, M.P., Atlassis, A.F., Pedrycz, W.: Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques. IEEE Transactions on Systems, Man, and Cybernetics: Part C 33, 297–312 (2003)CrossRefGoogle Scholar
  39. 39.
    Omar, N., Leite, A.S.: The learning process mediated by intelligent tutoring systems and conceptual learning. In: International Conference On Engineering Education, Rio de Janeiro, p. 20 (1998)Google Scholar
  40. 40.
    Fischetti, E., Gisolfi, A.: From computer-aided instruction to intelligent tutoring systems. Educational Technology 30(8), 7–17 (1990)Google Scholar
  41. 41.
    Winkels, R., Breuker, J.: What’s in an ITS? a functional decomposition. In: Costa, E. (ed.) New Directions for Intelligent Tutoring Systems. Spring, Berlin (1990)Google Scholar
  42. 42.
    Self, J.: The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of AI in Education 10, 350–364 (1999)Google Scholar
  43. 43.
    Sanders, J.R.: Presented at the 24th annu. meeting joint committee stand. educ. eval. (October 1998), http://www.jcsee.org/wp-content/uploads/2009/08/JCMinutes98.PDF

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • B. John Oommen
    • 1
  • M. Khaled Hashem
    • 1
  1. 1.School of Computer ScienceCarleton UniversityOttawaCanada

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