Representational Decisions When Learning Population Dynamics with an Instructional Simulation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2363)


DEMIST is a multi-representational simulation environment that supports understanding of the representations and concepts of population dynamics. We report on a study with 18 subjects with little prior knowledge that explored if DEMIST could support their learning and asked what decisions learners would make about how to use the many representations that DEMIST provides. Analysis revealed that using DEMIST for one hour significantly improved learners’ understanding of population dynamics though their knowledge of the relation between representations remained weak. It showed that learners used many of DEMIST’s features. For example, they investigated the majority of the representational space, used dyna-linking to explore the relation between representations and had preferences for representations with different computational properties. It also revealed that decisions made by designers impacted upon what is intended to be a free discovery environment.


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  1. [1]
    Cox, R. and Brna, P. (1995). Supporting the use of external representations in problem solving: the need for flexible learning environments. International Journal of Artificial Intelligence in Education 6(2/3): 239–302.Google Scholar
  2. [2]
    de Jong, T., Ainsworth, S., Dobson, M., van der Hulst, A., Levonen, J., Reimann, P., Sime, J., van Someren, M.W. and Spada, H. & Swaak, J. (1998). Acquiring knowledge in science and math: the use of multiple representations in technology based learning environments. Learning with Multiple Representations. M. W. van Someren et al. Oxford, Elsevier.Google Scholar
  3. [3]
    Van Labeke, N. and Ainsworth, S. (2001). Applying the DeFT Framework to the Design of Multi-Representational Instructional Simulations. AIED’2001-10th International Conference on Artificial Intelligence in Education, San Antonio, Texas, IOS Press.Google Scholar
  4. [4]
    Ainsworth, S. (1999). The functions of multiple representations. Computer & Education 33(2/3): 131–152.CrossRefGoogle Scholar
  5. [5]
    de Jong, T. and van Joolingen, W.R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research 68: 179–202.CrossRefGoogle Scholar
  6. [6]
    Kuyper, M., Knowledge engineering for usability: Model-Mediated Interaction Design of Authoring Instructional Simulations. Ph. D. Thesis. University of Amsterdam, (1998).Google Scholar
  7. [7]
    Gotelli, N.J. (1998). A Primer of Ecology. Sunderland, MA, Sinauer Associates.Google Scholar
  8. [8]
    Ainsworth, S., Bibby, P.A. and Wood, D.J. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences 11(1): 25–62.CrossRefGoogle Scholar
  9. [9]
    Tabachneck, H.J.M., Leonardo, A.M. and Simon, H.A. (1994). How does an expert use a graph?A model of visual & verbal inferencing in economics. 16th Annual Conference of the Cognitive Science Society, Hillsdale, NJ: LEA.Google Scholar
  10. [10]
    Schoenfeld, A.H., Smith, J.P. and Arcavi, A. (1993). Learning: the microgenetic analysis of one student’s evolving understanding of a complex subject matter domain. Advances in instructional psychology, volume 3. R. Glaser. Hillsdale, NJ, LEA. 3: 55–175.Google Scholar
  11. [11]
    Tabachneck-Schijf, H.J.M., Leonardo, A.M. and Simon, H.A. (1997). CaMeRa: A computational model of multiple representations. Cognitive Science 21(3): 305–350.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  1. 1.ESRC Centre for Research in Development, Instruction & Training School of PsychologyUniversity of NottinghamNottinghamUK

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