Do Preschoolers ‘Game the System’? A Case Study of Children’s Intelligent (Mis)Use of a Teachable Agent Based Play-&-Learn Game in Mathematics

  • Eva-Maria TernbladEmail author
  • Magnus Haake
  • Erik Anderberg
  • Agneta Gulz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10947)


For learning to take place in digital learning environments, learners need to use educational software – more or less – as intended. However, previous studies show that some school children, instead of trying to learn and master a skill, choose to systematically exploit or outsmart the system to gain progress. But what about preschoolers? The present study explores the presence of this kind of behavioral patterns among preschoolers who use a teachable agent-based play-&-learn game in early math. We analyzed behavioral data logs together with interviews and observations. We also analyzed action patterns deviating from the pedagogical design intentions in terms of non-harmful gaming, harmful gaming, and wheel-spinning. Our results reveal that even if pedagogically not intended use of the game did occur, harmful gaming was rare. Interestingly, the results also indicate an unexpected awareness in children of what it means to learn and to teach. Finally, we present a series of possible adjustments of the used software in order to decrease gaming-like behavior or strategies that signalize insufficient skills or poor learning.


Preschoolers Gaming the system Learning-by-teaching Teachable agent Wheel-spinning 



This research was financed by the Wallenberg Foundation.


  1. 1.
    Lindström, P., Gulz, A., Haake, M., Sjödén, B.: Matching and mismatching between the pedagogical design principles of a math game and the actual practices of play. J. Comput. Assist. Learn. 27, 90–102 (2011)CrossRefGoogle Scholar
  2. 2.
    Blair, K., Schwartz, D., Biswas, G., Leelawong, K.: Pedagogical agents for learning by teaching: teachable Agents. Educ. Technol. Soc. 47, 56–61 (2007). Special IssueGoogle Scholar
  3. 3.
    Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting student misuse of intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2004). Scholar
  4. 4.
    Beck, J.E., Gong, Y.: Wheel-spinning: students who fail to master a skill. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 431–440. Springer, Heidelberg (2013). Scholar
  5. 5.
    Baker, R.S.J.D., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum Comput Stud. 68(4), 223–241 (2010)CrossRefGoogle Scholar
  6. 6.
    Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K.: Why students engage in “gaming the system” behavior in interactive learning environments. J. Interact. Learn. Res. 19(2), 185–224 (2008)Google Scholar
  7. 7.
    Husain, L., Gulz, A., Haake, M.: Supporting early math– rationales and requirements for high quality software. J. Comput. Mathe. Sci. Teach. 34(4), 409–429 (2015)Google Scholar
  8. 8.
    Haake, M.: No child left behind, nor singled out – reasons for combining adaptive instruction and inclusive pedagogy in early math software (submitted)Google Scholar
  9. 9.
    Chase, C., Chin, D., Oppezzo, M., Schwartz, D.: Teachable agents and the protégé effect: increasing the effort towards learning. J. Sci. Educ. Technol. 18, 334–352 (2009)CrossRefGoogle Scholar
  10. 10.
    Wagster, J., Tan, J., Wu, Y., Biwas, G., Schwartz, D.: Do learning by teaching environments with metacognitive support help students develop better learning behaviors? In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 29, pp. 695–700 (2007)Google Scholar
  11. 11.
    Haake, M., Axelsson, A., Clausen-Bruun, M., Gulz, A.: Scaffolding mentalizing via a play-&-learn game for preschoolers. Comput. Educ. 90, 13–23 (2015)CrossRefGoogle Scholar
  12. 12.
    Axelsson, A., Andersson, R., Gulz, A.: Scaffolding executive function capabilities via play-&-learn software for preschoolers. J. Educ. Psychol. 108(7), 969–981 (2016)CrossRefGoogle Scholar
  13. 13.
    Griffin, S., Case, R., Siegler, R.: Classroom lessons: integrating cognitive theory and classroom practice. In: McGilly, K. (ed.) Rightstart: Providing the Central Conceptual Prerequisites for First Formal Learning of Arithmetic to Students at Risk for School Failure. MIT Press, Cambridge, pp. 25–50 (1994)Google Scholar
  14. 14.
    Baker, R.S.J.D., de Carvalho, A.M.J.A., Raspat, J., Aleven, V., Corbett, A.T., Koedinger, K.R.: Educational software features that encourage and discourage “gaming the system”. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 475–482 (2009)Google Scholar
  15. 15.
    Baker, R.S.J.D., Corbett, A.T., Koedinger, K.R.: The difficulty factors approach to the design of lessons in intelligent tutor curricula. Int. J. Artif. Intell. Educ. 17(4), 341–369 (2007)Google Scholar
  16. 16.
    Baker, R.S.J.D., et al.: Adapting to when students game an intelligent tutoring system. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 392–401. Springer, Heidelberg (2006). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Eva-Maria Ternblad
    • 1
    Email author
  • Magnus Haake
    • 1
  • Erik Anderberg
    • 1
  • Agneta Gulz
    • 1
  1. 1.Cognitive ScienceLund UniversityLundSweden

Personalised recommendations