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Social Organisation and Cooperative Learning: Identification and Categorisation of Groups and Sub-Groups in Non-Cooperative Games

  • Edward LongfordEmail author
  • Michael Gardner
  • Victor Callaghan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1044)

Abstract

This paper outlines the results of a Modified SYMLOG (Mod-SYMLOG) analysis for group formation, structure and interactions. While collaborative working has been an established working methodology for Education and Computer Science researchers alike, there has been a lack of focus in the latter as to what a group actually is within psychologically complex human communities. Here we discuss why groups can be beneficial to student learning in education, but also how misusing groups has negative effects. This paper presents the results of two board game based experiments. The first experiment used the classic SYMLOG model to show validity of the scenario in data collection and the second testing our Mod-SYMLOG. Results showed that Mod-SYMLOG was effective in capturing group dynamics, with indications of group structure.

Keywords

Collaborative learning Computer-supported collaborative learning 

References

  1. 1.
    Bales, R.F.: Interaction Process Analysis; A Method for the Study of Small Groups. Addison-Wesley, Oxford (1950)Google Scholar
  2. 2.
    Bartlett, R.L.: A flip of the coin-a roll of the die: an answer to the free-rider problem in economic instruction. J. Econ. Educ. 26(2), 131–139 (1995)Google Scholar
  3. 3.
    Berdun, F., Armentano, M., Berdun, L., Cincunegui, M.: Building symlog profiles with an online collaborative game. Int. J. Hum. Comput. Stud. (2018).  https://doi.org/10.1016/j.ijhcs.2018.07.002CrossRefGoogle Scholar
  4. 4.
    Blumenfeld, P.C., Marx, R.W., Soloway, E., Krajcik, J.: Learning with peers: from small group cooperation to collaborative communities. Educ. Researcher 25(8), 37–39 (1996)CrossRefGoogle Scholar
  5. 5.
    Calhamer, A.B.: The Rules of Diplomacy (2000). https://www.wizards.com/avalonhill/rules/diplomacy.pdf
  6. 6.
    Cohen, E.G.: Restructuring the classroom: conditions for productive small groups. Rev. Educ. Res. 64(1), 1–35 (1994)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dooley, J., Callaghan, V., Hagras, H., Gardner, M., Ghanbaria, M., AlGhazzawi, D.: The intelligent classroom: beyond four walls. In: Proceedings of the Intelligent Campus Workshop (IC 2011) held at the 7th IEEE Intelligent Environments Conference (IE 2011), Nottingham (2011)Google Scholar
  8. 8.
    Engel, D., Woolley, A.W., Jing, L.X., Chabris, C.F., Malone, T.W.: Reading the mind in the eyes or reading between the lines? Theory of mind predicts collective intelligence equally well online and face-to-face. PLoS ONE 9(12), e115212 (2014)CrossRefGoogle Scholar
  9. 9.
    Felemban, S., Gardner, M., Callaghan, V.: Towards recognising learning evidence in collaborative virtual environments: a mixed agents approach. Computers 6(3), 22 (2017)CrossRefGoogle Scholar
  10. 10.
    Forsyth, D.R.: Group Dynamics 15, (2014)Google Scholar
  11. 11.
    Gardner, M.R., Elliott, J.B.: The immersive education laboratory: understanding affordances, structuring experiences, and creating constructivist, collaborative processes, in mixed-reality smart environments. EAI Endorsed Trans. Future Intell. Educ. Environ. 1(1), e6 (2014)Google Scholar
  12. 12.
    Goodman, B., Linton, F., Gaimari, R.: Encouraging student reflection and articulation using a learning companion: a commentary. Int. J. Artif. Intell. Educ. 26(1), 474–488 (2016)CrossRefGoogle Scholar
  13. 13.
    Gunderson, D.E., Moore, J.D.: Group learning pedagogy and group selection. Int. J. Constr. Educ. Res. 4(1), 34–45 (2008)CrossRefGoogle Scholar
  14. 14.
    Jambi, E., Gardner, M., Callaghan, V.: Supporting mixed-mode role-play activities in a virtual environment. In: 2017 9th Computer Science and Electronic Engineering Conference, CEEC 2017 - Proceedings, pp. 49–54. IEEE, September 2017Google Scholar
  15. 15.
    Keyton, J., Wall, V.D.J.: Research instrument SYMLOG theory and method for measuring group and organizational communication. Manage. Commun. Q. 2(4), 544 (1989)CrossRefGoogle Scholar
  16. 16.
    List, C.: Group knowledge and group rationality: a judgment aggregation perspective. Episteme 2(01), 25–38 (2005)CrossRefGoogle Scholar
  17. 17.
    Longford, E., Gardner, M.R., Callaghan, V.: Group immersion in classrooms: a framework for an intelligent group-based tutoring system of multiple learners. In: Beck, D., et al. (eds.) Workshop, Long and Short Paper, and Poster Proceedings from the Fourth Immersive Learning Research Network Conference (iLRN 2018 Montana), pp. 133–135 (2018).  https://doi.org/10.3217/978-3-85125-609-3-20
  18. 18.
    Lonnqvist, J.E., Paunonen, S., Verkasalo, M., Leikas, S., Tuulio-Henriksson, A., Lonnqvist, J.: Personality characteristics of research volunteers. Eur. J. Pers. 21(8), 1017–1030 (2007)CrossRefGoogle Scholar
  19. 19.
    Olsen, Jennifer K., Belenky, Daniel M., Aleven, Vincent, Rummel, Nikol: Using an intelligent tutoring system to support collaborative as well as individual learning. In: Trausan-Matu, Stefan, Boyer, Kristy Elizabeth, Crosby, Martha, Panourgia, Kitty (eds.) ITS 2014. LNCS, vol. 8474, pp. 134–143. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07221-0_16CrossRefGoogle Scholar
  20. 20.
    Palmgren-Neuvonen, L., Korkeamäki, R.L.: Group interaction of primary-aged students in the context of a learner-generated digital video production. Learn. Cult. Soc. Inter. 3(1), 1–14 (2014)CrossRefGoogle Scholar
  21. 21.
    Rrafzadeh, A., Alexander, S., Dadgostar, F., Fan, C., Bigdeli, A.: How do you know that I don’t understand? A look at the future of intelligent tutoring systems. Comput. Hum. Behav. 24(4), 1342–1363 (2008)CrossRefGoogle Scholar
  22. 22.
    Salkind, N.: Encyclopedia of Research Design (2010)Google Scholar
  23. 23.
    Springer, L., Stanne, M.E., Donovan, S.S.: Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: a meta-analysis. Rev. Educ. Res. 69(1), 21–51 (1999)CrossRefGoogle Scholar
  24. 24.
    Stahl, G.: The group as paradigmatic unit of analysis: the contested relationship of CSCL to the learning sciences. Learn. Sci. Mapp. Terrain (2015)Google Scholar
  25. 25.
    Suebnukarn, S.: Intelligent tutoring system for medical problem-based learning. Prog. Educ. 18(18), 233–302 (2010)Google Scholar
  26. 26.
    Walker, E., Rummel, N., Koedinger, K.R.: Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. Int. J. Comput. Support. Collaborative Learn. 6(2), 279–306 (2011)CrossRefGoogle Scholar
  27. 27.
    Wallin, P.: Volunteer subjects as a source of sampling bias. Am. J. Sociol. 54(6), 539–544 (1949)CrossRefGoogle Scholar
  28. 28.
    Woolley, A.W.: Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004), 683–686 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Edward Longford
    • 1
    Email author
  • Michael Gardner
    • 1
  • Victor Callaghan
    • 1
  1. 1.University of EssexColchesterUK

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