Simulation of Learners’ Behaviors Based on the Modified Cellular Automata Model
Zhenyan Liang, Haiyan Liu, Chaoying Zhang, Shangyuan Yang
DOI: 10.4236/iim.2010.29065   PDF   HTML     3,583 Downloads   6,341 Views  


This study develops a computational model for simulation of behaviors of learners under the influence of motivation and engagement environment based on Cellular Automata (CA). It investigates the changing patterns of learners’ behaviors when motivation and engagement environment are assigned with different values respectively. The simulation process indicates that the internal factor, which is the motivation in this paper, plays a key role in changing learners’ behaviors under certain circumstance and the engagement environment also significantly influences learner’s perception. The results obtained also show good agreement with the phenomenon generally being observed in practice.

Share and Cite:

Z. Liang, H. Liu, C. Zhang and S. Yang, "Simulation of Learners’ Behaviors Based on the Modified Cellular Automata Model," Intelligent Information Management, Vol. 2 No. 9, 2010, pp. 563-568. doi: 10.4236/iim.2010.29065.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. J. Martin, “How Domain Specific is Motivation and Engagement across School, Sport, and Music? A Subst- Antive-Methodological Synergy Assessing Young Sportsp-Eople And Musicians,” Contemporary Eduction Psychology, Vol. 33, 2008, pp. 785-813.
[2] A. J. Martin, “Enhancing Student Motivation and En- Gagement:The Effects of a Multidimensional Intervention,” Contemporary Eduction Psychology, Vol. 33, No. 2, 2008, pp. 239-269.
[3] J. D Finn, “Withdrawing from School,” Review of Educational Research” Vol. 59, No. 2, pp. 117-142.
[4] F. D. Ritcher and D. Tjosvold, “Effects of Student Partciption in Classtoom Decision Making on Attitudes, Peer Interaction, Motivation and Learing,” Journal of Applied Phychology, Vol. 65, 1980, pp. 74-80.
[5] E. A. Anderman and M. L. Maehr, “Motivation and Schooling in the Middle Grades,” Review of Educational Research, Vol. 64, No. 2, pp. 287-310.
[6] J. A. Kelly and D. J. Hansen, (1987), “Social Interactions and Adjustment,” In V. B. Can Hasselt and M. Hersen, Ed., Handbook of adolescent psychology, Pergamon Press: Springer, New York, pp. 131-146.
[7] J. Freen, A. J. Martin and H. W. Marsh, “Motivation and Engagement in English, Mathematics and Science High School Subjects: Towards an Understanding of Multidimensional Domain Specificity,” Learning and Individual Differences, Vol. 17, No. 3, 2007, pp. 269-279.
[8] J. vonNeumann, “The General and Logical Theory of Automata,” L. A. Jiffries, Ed., Cerebral Mechanism in Behavior- the Hixon Symposium [C], Wiley, New York, 1951.
[9] H. Y. Liu, Y. L. Li, C. Y. Zhang and Q. Wang, “Simulation of Learners’ Behaviors Based on Cellular Automata,” IEEE 2009 International Conference on Computational Intelligence and Software Engineering, China, 2009, pp. 1-4.
[10] S. Wolfram, “Statistical Mechanics of Cellular Automata,” Reviews of Modern Physics, Vol. 55, No. 3, 1983, pp. 601-644.
[11] N. H. Packad, S. W. Fram, “Two Dimensional Cellular Automata,” Journal of Statistical Physics, Vol. 38, No. 5-6, 1985.
[12] J. Nemmann, “Theory of Self Reproducing Automata,” University of Illionois, Urbana, 1966.
[13] Frishu, Hasslacherb, Y. Pomeau,” Two Dimensional Cellular Automata,” Physical Review Letters, 1986.
[14] S. W. Fram, “Theory and Applications of Cellular Automata,” World Scientific, Singapore, 1986.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.