Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-06-01T18:13:14.163Z Has data issue: false hasContentIssue false

25 - Complex Systems and the Learning Sciences

Educational, Theoretical, and Methodological Implications

from Part V - Learning Disciplinary Knowledge

Published online by Cambridge University Press:  14 March 2022

By
Michael J. Jacobson  and
Uri Wilensky
Edited by
R. Keith Sawyer
R. Keith Sawyer
Affiliation:
University of North Carolina, Chapel Hill
Get access

Summary

A complex system is composed of many elements that interact with each other and their environment. The term emergence is used to describe how the large-scale features of the complex system arise from interactions between the components, and these system-level features are called emergent phenomena. This chapter reviews the multidisciplinary study of complex systems in physics, biology, and social sciences. This chapter reviews three topics: first, research on how people learn how to think about complex systems; second, how learning environments themselves can be analyzed as complex systems; and finally, how the analytic methods of complexity science – such as computer modeling – can be applied to the learning sciences. The chapter summarizes challenges and future opportunities for helping students learn about complex systems and for research in the learning sciences that considers educational systems to be complex phenomena.

Keywords

complexityemergencesystemsnonlinearityagent-based modelingself-organizationNetLogo
Type
Chapter
Information
The Cambridge Handbook of the Learning Sciences , pp. 504 - 522
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abrahamson, D., Blikstein, P., & Wilensky, U. (2007). Classroom model, model classroom: Computer-supported methodology for investigating collaborative-learning pedagogy. In Chin, C., Erkins, G., & Putambekar, S. (Eds.), Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference (Vol. 8, Part 1, pp. 45–55). New Brunswick, NJ.Google Scholar
Abrahamson, D., & Wilensky, U. (2004). ProbLab: A computer-supported unit in probability and statistics. In Hoines, M. J. & Fuglestad, A. B. (Eds.), Proceedings of the 28th Annual Meeting of the International Group for the Psychology of Mathematics Education (Vol. 1, p. 369). Norway: Bergen University College.Google Scholar
Abrahamson, D., & Wilensky, U. (2006). Is a disease like a lottery? Classroom networked technology that enables student reasoning about complexity. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.Google Scholar
Anderson, J. R., Greeno, J. G., Reder, L. M., & Simon, H. A. (2000). Perspectives on learning, thinking, and activity. Educational Researcher, 29(4), 11–13. doi:10.3102/0013189X029004011Google Scholar
Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning and education. Educational Researcher, 25(4), 5–11.CrossRefGoogle Scholar
Anderson, J. R., Reder, L. M., & Simon, H. A. (1997). Situative versus cognitive perspectives: Form versus substance. Educational Researcher, 26(1), 18–21.Google Scholar
Arastoopour Irgens, G., Dabholkar, S., Bain, C., et al. (2020). Modeling and measuring students’ computational thinking practices in science. Journal of Science Education and Technology, 29(1), 137–161.CrossRefGoogle Scholar
Arthur, B., Durlauf, S., & Lane, D. (Eds.). (1997). The economy as an evolving complex system (Vol. II). Reading, MA: Addison-Wesley.Google Scholar
Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of 1/f noise. Physical Review Letters, 59(4), 381–384. doi: 10.1103/PhysRevLett.59.381CrossRefGoogle Scholar
Balmer, M., Nagel, K., & Raney, B. (2004). Large-scale multi-agent simulations for transportation applications. Intelligent Transportation Systems, 8(4), 1–17.Google Scholar
Bar-Yam, Y. (1997). Dynamics of complex systems. Reading, MA: Addison-Wesley.Google Scholar
Bereiter, C., & Scardamalia, M. (2006). Education for the knowledge age: Design-centered models of teaching and instruction. In Alexander, P. A. & Winne, P. H. (Eds.), Handbook of educational psychology (2nd ed., pp. 695–713). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Bishop, B. A., & Anderson, C. W. (1990). Student conceptions of natural selection and its role in evolution. Journal of Research in Science Teaching, 27(5), 415–427.CrossRefGoogle Scholar
Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using multi-agent simulation. International Journal of Computers for Mathematical Learning, 14(1), 81–119.CrossRefGoogle Scholar
Blikstein, P., & Wilensky, U. (2010). MaterialSim: A constructionist agent-based modeling approach to engineering education. In Jacobson, M. J. & Reimann, P. (Eds.), Designs for learning environments of the future: International perspectives from the learning sciences (pp. 17–60). New York, NY: Springer.CrossRefGoogle Scholar
Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2015). Sandboxes for model-based inquiry. Journal of Science Education and Technology, 24(2), 265–286.Google Scholar
Brown, D. E., & Hammer, D. (2008). Conceptual change in physics. In Vosniadou, S. (Ed.), Handbook of research on conceptual change (pp. 127–154). Hillsdale, NJ: Laurance Erlbaum Associates.Google Scholar
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.CrossRefGoogle Scholar
Casti, J. L. (1994). Complexificantion: Explaining a paradoxical world through the science of surprise. New York, NY: HarperCollins.Google Scholar
Charles, E. S. (2002). Using complex systems thinking to facilitate shifts in ontological beliefs: A qualitative case study systematically investigating a learning and teaching context that employs “StarLogo” simulations and a one-on-one coaching methodology. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.Google Scholar
Charles, E. S., & d’Apollonia, S. (2004). Developing a conceptual framework to explain emergent causality: Overcoming ontological beliefs to achieve conceptual change. In Forbus, K., Gentner, D., & Reiger, T. (Eds.), Proceedings of the 26th Annual Cognitive Science Society (pp. 210–215). Mahwah, NJ: Lawrence Erlbaum Associates. Retrieved from www.cogsci.northwestern.edu/cogsci2004/sessions.html#emergentGoogle Scholar
Chi, M. T. H., Roscoe, R. D., Slotta, J. D., Roy, M., & Chase, C. C. (2012). Misconceived causal explanations for emergent processes. Cognitive Science, 36(1), 1–61. doi:10.1111/j.1551-6709.2011.01207CrossRefGoogle ScholarPubMed
Cobb, P., & Bowers, J. (1999). Cognitive and situated learning perspectives in theory and practice. Educational Researcher, 28(2), 4–15.CrossRefGoogle Scholar
Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive dynamic modeling. Journal of the Learning Sciences, 9(4), 471–500.CrossRefGoogle Scholar
Cuevas, E. (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computational Biology Medicine, 121, Article 103827. doi:10.1016/j.compbiomed.2020.103827CrossRefGoogle ScholarPubMed
Dabholkar, S., & Wilensky, U. (2019). Designing ESM-mediated collaborative activity systems for science learning. Paper presented at the Proceedings of International Conference of Computer Supported Collaborative Learning 2019, Lyon, France.Google Scholar
Dai, L., Vorsellen, D., Korolev, K., & Gore, J. (2012). Generic indicators for loss of resilience before a tipping point leading to population collapse. Science, 336(6085), 1175–1177. doi:10.1126/science.1219805Google Scholar
Edwards, L. (1995). Microworlds as representations. In diSessa, A. A., Hoyles, C., Noss, R., & Edwards, L. D. (Eds.), Computers and exploratory learning (pp. 127–154). New York, NY: Springer.CrossRefGoogle Scholar
Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.Google Scholar
Evans, E. M. (2013). Conceptual change and evolutionary biology: Taking a developmental perspective. In Vosniadou, S. (Ed.), International handbook of research on conceptual change (2nd ed., pp. 220–239). New York, NY: Routledge.Google Scholar
Frank, K. A., Zhao, Y., & Borman, K. (2004). Social capital and the diffusion of innovations within organizations: Application to the implementation of computer technology in schools. Sociology of Education, 77(2), 148–171.CrossRefGoogle Scholar
Gladwell, M. (2000). The tipping point: How little things can make a big difference. Boston, MA: Little, Brown & Co.Google Scholar
Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer through complex systems principles. Journal of the Learning Sciences, 17(4), 465–516.Google Scholar
Greeno, J. G. (1997). On claims that answer the wrong questions. Educational Researcher, 26(1), 5–17.Google Scholar
Guo, Y., & Wilensky, U. (2018). Mind the gap: Teaching high school students about wealth inequality through agent-based participatory simulations. In Dagiene, V. & Jasute, E. (Eds.), Proceedings of Constructionism 2018. Vilnius, Lithuania.Google Scholar
Hjorth, A., Brady, C., Head, B., & Wilensky, U. (2015). Thinking within and between levels: Exploring reasoning with multi-level linked models. In Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference. Gothenburg, Sweden.Google Scholar
Hjorth, A., Head, B., Brady, C., & Wilensky, U. (2020). LevelSpace – A NetLogo extension for multi-level agent-based modeling. Journal of Artificial Societies and Social Simulation, 23(1).CrossRefGoogle Scholar
Hjorth, A., & Wilensky, U. (2014). Re-grow your city – A NetLogo curriculum unit on regional development. In Polman, J. L., Kyza, E. A., O’Neill, D. K., et al. (Eds.), The International Conference of the Learning Sciences (ICLS) 2014 (Vol. 3, pp. 1553–1554). International Society of the Learning Sciences.Google Scholar
Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16(3), 307–331.Google Scholar
Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Reading, MA: Addison-Wesley.Google Scholar
Hsiao, L., Lee, I., & Klopfer, E. (2019). Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning. British Journal of Educational Technology, 50, 2203–2216. doi:10.1111/bjet.12844Google Scholar
Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.Google Scholar
Jacobson, M. J., Kapur, M., & Reimann, P. (2016). Conceptualizing debates in learning and educational research: Towards a complex systems conceptual framework of learning. Educational Psychologist, 51(2), 210–218. doi:10.1080/00461520.2016.1166963CrossRefGoogle Scholar
Jacobson, M. J., Kapur, M., So, H.-J., & Lee, J. (2011). The ontologies of complexity and learning about complex systems. Instructional Science, 39, 763–783. doi:10.1007/s11251-010-9147-0Google Scholar
Jacobson, M. J., Kim, B., Pathak, S., & Zhang, B. (2015). To guide or not to guide: Issues in the sequencing of pedagogical structure in computational model-based learning. Interactive Learning Environments, 23(6), 715–730. doi:10.1080/10494820.2013.792845Google Scholar
Jacobson, M. J., Levin, J. A., & Kapur, M. (2019). Education as a complex system: Conceptual and methodological implications. Educational Researcher, 48(2), 112–119. doi:10.3102/0013189x19826958Google Scholar
Jacobson, M. J., Markauskaite, L., Portolese, A., Kapur, M., Lai, P. K., & Roberts, G. (2017). Designs for learning about climate change as a complex system. Learning and Instruction, 52, 1–14. doi:10.1016/j.learninstruc.2017.03.007CrossRefGoogle Scholar
Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34.CrossRefGoogle Scholar
Kapur, M. (2006). Productive failure. Cognition and Instruction, 26(3), 307–313.Google Scholar
Kapur, M. (2014). Productive failure in learning math. Cognitive Science, 38(5), 1008–1022. doi:10.1111/cogs.12107CrossRefGoogle ScholarPubMed
Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. Journal of the Learning Sciences, 21(1), 45–83. doi:10.1080/10508406.2011.591717Google Scholar
Kauffman, S. (1995). At home in the universe: The search for laws of self-organization and complexity. New York. NY: Oxford University Press.Google Scholar
Kitano, H. (2002). Computational systems biology. Nature, 420(6912), 206–210.Google Scholar
Kozma, R. B., Chin, E., Russell, J., & Marx, N. (2000). The role of representations and tools in the chemistry laboratory and their implications for chemistry learning. Journal of the Learning Sciences, 9(3), 105–144.Google Scholar
Lemke, J., & Sabelli, N. (2008). Complex systems and educational change: Towards a new research agenda. Educational Philosophy and Theory, 40(1), 118–129. doi:10.1111/j.1469-5812.2007.00401.xCrossRefGoogle Scholar
Levy, S. T., & Wilensky, U. (2008). Inventing a “mid-level” to make ends meet: Reasoning through the levels of complexity. Cognition & Instruction, 26(1), 1–47.Google Scholar
Levy, S. T., & Wilensky, U. (2009). Students’ learning with the Connected Chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243–254. doi:10.1007/s10956-009-9145-7CrossRefGoogle Scholar
Loibl, K., & Rummel, N. (2014). Knowing what you don’t know makes failure productive. Learning and Instruction, 34, 74–85. doi:10.1016/j.learninstruc.2014.08.004Google Scholar
Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of Atmospheric Science, 20(2), 130–141.Google Scholar
Maroulis, S., & Gomez, L. (2008). Does ‘connectedness’ matter? Evidence from a social network analysis of a small school reform. Teachers College Record, 110(9), 1901–1929.CrossRefGoogle Scholar
Maroulis, S., Guimerà, R., Petry, H., et al. (2010). Complex systems view of educational policy research. Science, 330(6000), 38–39.CrossRefGoogle ScholarPubMed
Mitchell, M. (2009). Complexity: A guided tour. New York, NY: Oxford University Press.Google Scholar
Page, S. (2011). Diversity and complexity. Princeton, NJ: Princeton University Press.Google Scholar
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.Google Scholar
Penner, D. E. (2001). Complexity, emergence, and synthetic models in science education. In Crowley, K., Schunn, C. D., & Okada, T. (Eds.), Designing for science (pp. 177–208). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Resnick, M. (1996). Beyond the centralized mindset. Journal of the Learning Sciences, 5(1), 1–22.Google Scholar
Resnick, M., & Wilensky, U. (1993). Beyond the deterministic, centralized mindsets: A new thinking for new science. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.Google Scholar
Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing probabilistic decentralized thinking through role-playing activities. Journal of Learning Science, 7(2), 153–172.Google Scholar
Samarapungavan, A., & Wiers, R. W. (1997). Children’s thoughts on the origin of species: A study of explanatory coherence. Cognitive Science, 21(2), 147–177.Google Scholar
Sawyer, R. K. (2005). Social emergence: Societies as complex systems. New York, NY: Cambridge University Press.Google Scholar
Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.Google Scholar
Stieff, M., & Wilensky, U. (2003). Connected chemistry: Incorporating interactive simulations into the chemistry classroom. Journal of Science Education and Technology, 12(3), 285–302.Google Scholar
Vermeer, W., Hjorth, A., Jenness, S. M., Brown, C. H., & Wilensky, U. (2020). Leveraging modularity during replication of high-fidelity models: Lessons from replicating an agent-based model for HIV prevention. Journal of Artificial Societies and Social Simulation, 23(4), Article 7. doi:10.18564/jasss.4352CrossRefGoogle ScholarPubMed
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.CrossRefGoogle ScholarPubMed
West, J. J., & Dowlatabadi, H. (1999). On assessing the economic impacts of sea-level rise on developed coasts. In Downing, T. E., Olsthoorn, A. A., & Tol, R. S. J. (Eds.), Climate change and risk (pp. 205–220). New York, NY: Routledge.Google Scholar
Wilensky, U. (1997). StarLogoT. Evanston, IL: Center for Connected Learning and Computer Based Modeling, Northwestern University. Retrieved from ccl.northwestern.edu/cmGoogle Scholar
Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling. Northwestern University. Retrieved from ccl.northwestern.edu/netlogoGoogle Scholar
Wilensky, U. (2001). Modeling nature’s emergent patterns with multi-agent languages. Paper presented at the EuroLogo 2001 conference, Linz, Austria.Google Scholar
Wilensky, U. (2003). Statistical mechanics for secondary school: The GasLab modeling toolkit. International Journal of Computers for Mathematical Learning, 8(1), 1–41.Google Scholar
Wilensky, U. (2020). Restructurations: Reformulating knowledge domains through new representational infrastructure. In Holbert, N., Berland, M., & Kafai, Y. (Eds.), Designing constructionist futures: The art, theory, and practice of learning designs (pp. 287–300). Cambridge, MA: MIT Press.Google Scholar
Wilensky, U., Hazzard, E., & Longenecker, S. (2000). A bale of turtles: A case study of a middle school science class studying complexity using StarLogoT. Paper presented at the meeting of the Spencer Foundation, New York, October 11–13, 2000.Google Scholar
Wilensky, U., & Novak, M. (2010). Teaching and learning evolution as an emergent process: Learning with agent-based models of evolutionary dynamics. In Taylor, R. S. & Ferrari, M. (Eds.), Epistemology and science education: Understanding the evolution vs. intelligent design controversy (pp. 213–242). New York, NY: Routledge.Google Scholar
Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines through new representational forms. In Clayson, J. & Kalas, I. (Eds.), Proceedings for Constructionism 2010 (p. 97). Paris, France.Google Scholar
Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Cambridge, MA: MIT Press.Google Scholar
Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories. Cognition and Instruction, 24(2), 171–209.CrossRefGoogle Scholar
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.Google Scholar
Wilensky, U., & Stroup, W. (2000). Networked gridlock: Students enacting complex dynamic phenomena with the HubNet architecture. In Fishman, B. & O’Connor-Divelbiss, S. (Eds.), Fourth International Conference of the Learning Sciences (pp. 282–289). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Wilkerson-Jerde, M. H., & Wilensky, U. (2015). Patterns, probabilities, and people: Making sense of quantitative change in complex systems. Journal of the Learning Sciences, 24(2), 204–251. doi:10.1080/10508406.2014.976647CrossRefGoogle Scholar
Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.Google Scholar
Yoon, S. A. (2008). An evolutionary approach to harnessing complex systems thinking in the science and technology classroom. International Journal of Science Education, 30(1), 1–32.CrossRefGoogle Scholar
Yoon, S. A., Anderson, E., Klopfer, E., et al. (2016). Designing computer-supported complex systems curricula for the Next Generation Science Standards in high school science classrooms. Systems, 4(38).Google Scholar
Yoon, S. A., Goh, S.-E., & Park, M. (2018). Teaching and learning about complex systems in K–12 science education: A review of empirical studies 1995–2015. Review of Educational Research, 88(2), 285–325.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×