Teaching Simulation on Collaborative Learning by the Complex Doubly Structural Network

  • Setsuya Kurahashi
  • Keisuke Kuniyoshi
  • Takao Terano
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 296)


In this research, a teaching simulation model is built where the understanding status, knowledge structure, and collaborative effect of each learner are integrated by using a doubly structural network model. The purpose of the model is to analyse the actual conditions of understanding of learners regarding instructions given in classrooms. The influence of teaching strategies on learning effects is analysed in the model. Moreover, the influence of the seating arrangement of learners on collaborative learning effects is discussed. As a result of the simulation, the following points were found: (1) the learning effects depend on the difference in teaching strategies, and (2) a teaching strategy where learning skills, material structure, and collaborative learning are integrated on a doubly structural network model is the most effective.


teaching strategy collaborative learning classroom item response theory (IRT) social simulation a doubly structural network model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Durlach, P.J., Lesgold, A.M. (eds.): Adaptive Technologies for Training and Education. Cambridge University Press, New York (2012)Google Scholar
  2. 2.
    Sawyer, K.: Introduction, The New Science of Learning. In: Sawyer, K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 1–18. Cambridge University Press (2006)Google Scholar
  3. 3.
    Baker, R.S., Yacef, K.: The State of Educational Data Mining in 2009, A Review and Future Visions. Journal of Educational Data Mining 1(1), 3–17 (2009)Google Scholar
  4. 4.
    Johnson, D.W., Johnson, R.T., Holubec, E.J.: Circles of Learning: Cooperation in the classroom, 5th edn. Interaction Book Company (2002)Google Scholar
  5. 5.
    Terano, T.: A Doubly Structural Network Model. The Operations Research Society of Japan 2(1), 57–69 (2008)Google Scholar
  6. 6.
    Kunnigami, M., Kobayashi, M., Yamadera, S., Terano, T.: A Doubly Structural Network Model and Analysis on Emergence of Money. Information Processing Society of Japan, Mathematical Modeling and Problem Solving 53(12), 661–666 (2009)Google Scholar
  7. 7.
    Kunnigami, M., Kobayashi, M., Yamadera, S., Yamada, T., Terano, T.: A Doubly Structural Network Model and Analysis on Emergence of Money. In: Takadama, K., Cioffi-Revilla, C., Defuant, G. (eds.) Simulating Interacting Agents and Social Phenomena, Agent-Based Social Systems, vol. 7, pp. 137–149. Springer, Tokyo (2010)CrossRefGoogle Scholar
  8. 8.
    Birnbaum, A.: Some Latent Trait Models. In: Load, F.M., Novick, M.R. (eds.) Statistical Theories of Mental Test Sores, pp. 97–424. Addison-Wesley, Reading (1968)Google Scholar
  9. 9.
    Ueno, M., Shojima, K.: The New Trend of Learning Evaluation. Asakura Shoten, Tokyo (2010)Google Scholar
  10. 10.
    Toyoda, H.: Item Latent Theory (Beginners’ Course). Asakura Shoten, Tokyo (2012)Google Scholar
  11. 11.
    Ueno, M.: An Extension of the IRT to a Network Model. Behaviormetrika 29(1), 59–79 (2002)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Wainer, H., Bradlow, E.T., Wang, X.: Testlet Response Theory and Its Appliations. Cambridge University Press, New York (2007)Google Scholar
  13. 13.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)Google Scholar
  14. 14.
    Shigemasu, K., Motomura, Y., Ueno, M.: General Information of Bayesian Network. Baifukan, Tokyo (2006)Google Scholar
  15. 15.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, Berlin (2007)CrossRefMATHGoogle Scholar
  16. 16.
    Koller, D., Friedman, N.: Probabilistic Graphical Models. The MIT Press, MA (2009)Google Scholar
  17. 17.
    Ueno, M., Onishi, M., Shigemasu, H.: An Extension of the IRT to a Network Model. Information Processing Society of Japan (A), J77-A(10), 1398–1408 (1994)Google Scholar
  18. 18.
    Ueno, M.: The Graphical Test Theory from Bayesian Approach. Japan Society for Educational Technology 24(1), 35–52 (2000)Google Scholar
  19. 19.
    Epstein, J., Axtell, R.: Growing Artificial Societies. Brookings Institution Press, Washington, D.C. (1996)Google Scholar
  20. 20.
    Axelrod, R.: The Complexity of Cooperation. Princeton University Press, New Jersey (1999)Google Scholar
  21. 21.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Setsuya Kurahashi
    • 1
  • Keisuke Kuniyoshi
    • 1
  • Takao Terano
    • 2
  1. 1.Graduate School of Business SciencesUniversity of TsukubaTokyoJapan
  2. 2.Tokyo Institute of Technology, Computational Intelligence and Systems ScienceYokohamaJapan

Personalised recommendations