Instructional Science

, Volume 39, Issue 5, pp 763–783 | Cite as

The ontologies of complexity and learning about complex systems

  • Michael J. JacobsonEmail author
  • Manu Kapur
  • Hyo-Jeong So
  • June Lee


This paper discusses a study of students learning core conceptual perspectives from recent scientific research on complexity using a hypermedia learning environment in which different types of scaffolding were provided. Three comparison groups used a hypermedia system with agent-based models and scaffolds for problem-based learning activities that varied in terms of the types of text based scaffolds that were provided related to a set of complex systems concepts. Although significant declarative knowledge gains were found for the main experimental treatment in which the students received the most scaffolding, there were no significant differences amongst the three groups in terms of the more cognitively demanding performance on problem solving tasks. However, it was found across all groups that the students who enriched their ontologies about how complex systems function performed at a significantly higher level on transfer problem solving tasks in the posttest. It is proposed that the combination of interactive representational scaffolds associated with NetLogo agent-based models in complex systems cases and problem solving scaffolding allowed participants to abstract ontological dimensions about how systems of this type function that, in turn, was associated with the higher performance on the problem solving transfer tasks. Theoretical and design implications for learning about complex systems are discussed.


Learning about complex systems Ontology Hypermedia Agent-based models Scaffolding Problem based learning Digital media Conceptual change Knowledge transfer 



The research discussed in this paper has been funded in part by support to the first author from the University of Sydney Faculty of Education and Social Work, the Singapore Ministry of Education to the Learning Sciences Laboratory at the National Institute of Education (NIE), Nanyang Technological University (NTU), and from the Korean IT Industry Promotion Agency. Phoebe Chen Jacobson designed the Complex Systems Knowledge Mediator hypermedia learning environment used in this study. The contributions of the research assistants to this project are also gratefully acknowledged: Seo-Hong Lim, Lynn Low, and HyungShin Kim.


  1. Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition: Implications for the design of computer-based scaffolds. Instructional Science, 33, 367–379.CrossRefGoogle Scholar
  2. Bar-Yam, Y. (1997). Dynamics of complex systems. Reading, MA: Addison-Wesley.Google Scholar
  3. Bransford, J. D., Brown, A. L., Cocking, R. R., & Donovan, S. (Eds.). (2000). How people learn: Brain, mind, experience, and school (expanded edition). Washington, DC: National Academy Press.Google Scholar
  4. Casti, J. L. (1994). Complexificantion: Explaining a paradoxical world through the science of surprise. New York: HarperCollins.Google Scholar
  5. Charles, E. S. (2003). An ontological approach to conceptual change: The role that complex systems thinking may play in providing the explanatory framework needed for studying contemporary sciences. Unpublished doctoral dissertation, Concordia University, Montreal, Canada.Google Scholar
  6. Charles, E. S., & d’Apollonia, S. (2004). Developing a conceptual framework to explain emergent causality: Overcoming ontological beliefs to achieve conceptual change. In K. Forbus, D. Gentner, & T. Reiger (Eds.), Proceedings of the 26th annual cognitive science society. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  7. Chi, M. T. H. (1992). Conceptual change within and across ontological categories: Implications for learning and discovery in science. In R. Giere (Ed.), Minnesota studies in the philosophy of science: Cognitive models of science (Vol. XV, pp. 129–186). Minneapolis: University of Minnesota Press.Google Scholar
  8. Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences, 14(2), 161–199.CrossRefGoogle Scholar
  9. Chi, M. T. H., Slotta, J. D., & de Leeuw, N. (1994). From things to processes: A theory of conceptual change for learning science concepts. Learning and Instruction, 4, 27–43.CrossRefGoogle Scholar
  10. Collins, A., Brown, J., & Newman, S. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. Resnick (Ed.), Knowing, learning, and instruction (pp. 453–494). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  11. Davis, E. A., & Miyake, N. (2004). Explorations of scaffolding in complex classroom systems. The Journal of the Learning Sciences, 13(3), 265–272.CrossRefGoogle Scholar
  12. diSessa, A. A. (2006). A history of conceptual change research: Threads and fault lines. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 265–281). Cambridge, UK: Cambridge University Press.Google Scholar
  13. Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). (2007). Taking science to school: Learning and teaching science in grades K08. Washington, DC: The National Academies Press.Google Scholar
  14. Gell-Mann, M. (1994). The quark and the jaguar: Adventures in the simple and the complex. New York: Freeman and Company.Google Scholar
  15. Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–408.CrossRefGoogle Scholar
  16. Goldstone, R. L. (2006). The complex systems see-change in education. The Journal of the Learning Sciences, 15(1), 35–43.CrossRefGoogle Scholar
  17. Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer through complex systems principles. Journal of the Learning Sciences, 17(4), 465–516.CrossRefGoogle Scholar
  18. Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. The Journal of the Learning Sciences, 16(3), 307–331.CrossRefGoogle Scholar
  19. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Reading, MA: Addison-Wesley.Google Scholar
  20. Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.CrossRefGoogle Scholar
  21. Jacobson, M. J. (2008). Hypermedia systems for problem-based learning: Theory, research, and learning emerging scientific conceptual perspectives. Educational Technology Research and Development, 56, 5–28.CrossRefGoogle Scholar
  22. Jacobson, M. J., & Archodidou, A. (2000). The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge. The Journal of the Learning Sciences, 9(2), 149–199.CrossRefGoogle Scholar
  23. Jacobson, M. J., Maouri, C., Mishra, P., & Kolar, C. (1996). Learning with hypertext learning environments: Theory, design, and research. Journal of Educational Multimedia and Hypermedia, 5(3/4), 239–281.Google Scholar
  24. Jacobson, M. J., & Spiro, R. J. (1993). Hypertext learning environments and cognitive flexibility: Research into the transfer of complex knowledge (Technical No. 573). Champaign, IL: University of Illinois Center for the Study of Reading.Google Scholar
  25. Jacobson, M. J., & Spiro, R. J. (1995). Hypertext learning environments, cognitive flexibility, and the transfer of complex knowledge: An empirical investigation. Journal of Educational Computing Research, 12(5), 301–333.CrossRefGoogle Scholar
  26. Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the Learning Sciences, 15(1), 11–34.CrossRefGoogle Scholar
  27. Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.CrossRefGoogle Scholar
  28. Kapur, M. (2009). Productive failure in mathematical problem solving. Instructional Science. doi: 10.1007/s11251-11009-19093-x.
  29. Kapur, M. (2010). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science. doi: 10.1007/s11251-11010-19144-11253.
  30. Kauffman, S. (1995). At home in the universe: The search for laws of self-organization and complexity. New York: Oxford University Press.Google Scholar
  31. Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. Chicago: University of Chicago Press.Google Scholar
  32. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.CrossRefGoogle Scholar
  33. Levin, J. A., Stuve, M. J., & Jacobson, M. J. (1999). Teachers’ conceptions of the internet and the world wide web: A representational toolkit as a model of expertise. Journal of Educational Computing Research, 21(1), 1–23.Google Scholar
  34. Parunak, H. V. D., Savit, R., & Riolo, R. L. (1998). Agent-based modeling vs. equation-based modeling: A case study and users’ guide. In Proceedings of multi-agent systems and agent-based simulation (MABS’98) (pp. 10–25). Heidelberg: Springer-Verlag.Google Scholar
  35. Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press.Google Scholar
  36. Resnick, M., & Wilensky, U. (1993). Beyond the deterministic, centralized mindsets: A new thinking for new science. In Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.Google Scholar
  37. Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing probabilistic decentralized thinking through role-playing activities. Journal of Learning Science, 7(2), 153–172.CrossRefGoogle Scholar
  38. Sabelli, N. (2006). Understanding complex systems strand: Complexity, technology, science, and education. The Journal of the Learning Sciences, 15(1), 5–9.CrossRefGoogle Scholar
  39. Tergan, S. O. (1997). Conceptual and methodological shortcomings in hypertext/hypermedia design and research. Journal of Educational Computing Research, 16(3), 209–235.CrossRefGoogle Scholar
  40. Thompson, K. (2008). The value of multiple representations for learning about complex systems. In Paper presented at the International Conference for the Learning Sciences, Utrecht, The Netherlands.Google Scholar
  41. Vosniadou, S., & Brewer, W. F. (1994). Mental models of the day/night cycle. Cognitive Science, 18(1), 123–183.CrossRefGoogle Scholar
  42. Wilensky, U. (1998). NetLogo wolf sheep predation model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. (
  43. Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling. Northwestern University (
  44. Wilensky, U., & Reisman, K. (1998). Learning biology through constructing and testing computational theories: An embodied modeling approach. In Paper presented at the Second International Conference on Complex Systems, Nashau, NH.Google Scholar
  45. Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition & Instruction, 24(2), 171–209.CrossRefGoogle Scholar
  46. Wilensky, U., & Resnick, M. (1995). New thinking for new sciences: Constructionist approaches for exploring complexity. In Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.Google Scholar
  47. Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.CrossRefGoogle Scholar
  48. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Psychology and Psychiatry, 17, 89–100.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Michael J. Jacobson
    • 1
    Email author
  • Manu Kapur
    • 2
  • Hyo-Jeong So
    • 2
  • June Lee
    • 2
  1. 1.Centre for Research on Computer Supported Learning and Cognition (CoCo), Faculty of Education and Social WorkThe University of SydneySydneyAustralia
  2. 2.Learning Sciences Laboratory, National Institute of EducationNanyang Technological UniversitySingaporeSingapore

Personalised recommendations