Advertisement

Group Formation for Minimizing Bullying Probability. A Proposal Based on Genetic Algorithms

  • L. Pedro Salcedo
  • M. Angélica Pinninghoff J.
  • A. Ricardo Contreras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

Bullying is a problem that needs to be considered in the early stages of group formation. Unfortunately, as far as we are aware, there is not known procedure helping teachers to cope with this problem. It has been established that, in a certain group, a specific configuration in the students distribution affects the behavior among them. Based on this fact, we propose the use of genetic algorithms for helping in students distribution in a classroom, taking into account elements like leadership traits among other features. The sociogram is a technique that teachers have been using for years for supporting group formation. The sociogram is a sociometric diagram representing the pattern of relationships among individuals in a group, usually expressed in terms of which persons they prefer to associate with. This work combines the concepts of genetic algorithms and sociograms, that can be easily represented by means of relationships graphs. A set of tests is applied to the students to collect relevant data, and results can be validated with the help of specialists.

Keywords

Bullying Genetic algorithms Sociograms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ani, Z.C., Yasin, A., Husin, M.Z., Hamid, Z.A.: A Method for Group Formation Using Genetic Algorithm. International Journal on Computer Science and Engineering 02(09), 3060–3064 (2010)Google Scholar
  2. 2.
    Farrington, D., Baldry, A., Kyvsgaard, B., Ttofi, M.: Effectiveness of Programs to Prevent School Bullying. Institute of Criminology, Sidgwick Avenue, Cambridge CB3 9DT, UK (2008)Google Scholar
  3. 3.
    Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence. Theories, Methods, and Technologies. The MIT Press, Cambridge (2008)Google Scholar
  4. 4.
    Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics. Springer, Heidelberg (2000)CrossRefzbMATHGoogle Scholar
  5. 5.
    Poon, P.W., Carter, J.N.: Genetic algorithm crossover operators for ordering applications. Computer Ops. Res., 135–147 (1995)Google Scholar
  6. 6.
    Vreeman, R.C., Carroll, A.E.: A systematic review of school-based interventions to prevent bullying. Archives of Pediatric and Adolescent Medicine 161, 78–88 (2007)CrossRefGoogle Scholar
  7. 7.
    Wolke, D.: Bullying: Facts and Processes. University of Warwick institutional repository (2010), http://www.wdms.org/publications.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • L. Pedro Salcedo
    • 1
  • M. Angélica Pinninghoff J.
    • 2
  • A. Ricardo Contreras
    • 2
  1. 1.Research and Educational Informatics DepartmentUniversity of ConcepciónChile
  2. 2.Department of Computer ScienceUniversity of ConcepciónChile

Personalised recommendations