Complexity and the Learning Sciences


Contributor: Michael J. Jacobson

Over the past 15 years, there has been increasing interest in conceptual perspectives and methods being employed in the study of complex physical and social systems to inform research in the learning sciences and education more broadly. Learning scientists involved with this research topic typically view the contexts in which learning occurs as being complex systems with elements or agents at different levels—including neuronal, cognitive, intrapersonal, interpersonal, cultural—in which there are feedback interactions within and across levels of the systems so that collective properties arise (i.e., emerge) from the behaviors of the parts, often with properties that are not individually exhibited by those parts. Complexity conceptual perspectives have been used to inform theorizing in the learning sciences and CSCL fields and to employ innovative methods for conducting learning sciences research such as the use of agent-based and network topology modeling tools. Considerable research has also been done into how students can learn complexity and complex systems ideas—many of which pose conceptual challenges for students to understand—that are now being required in many STEM classes and national curricula such as the United States Next Generation Science Standards.

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Video Resources

Listen to the Complexity and the Learning Sciences webinar


  • Jacobson, M. J., Kapur, M., & Reimann, P. (2014). Towards a complex systems meta-theory of learning as an emergent phenomenon: Beyond the cognitive versus situative debate. To be presented at the 2014 International Conference of the Learning Sciences.
  • 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. [Access Online]

Learning Scientists Who Have Researched This Topic

  • Cindy Hmelo-Silver
  • Michael J. Jacobson
  • Manu Kapur
  • Peter Reimann
  • Mitchel Resnick
  • R. Keith Sawyer
  • Uri Wilensky