Learning about complexity and beyond: Theoretical and methodological implications for the learning sciences

This paper provides an overview of a symposium that explored the implications of complexity for the field of the learning sciences. Two papers explored aspects of learning about complex systems in the domains of physics and electricity and of the mathematics of change and variation. The third paper viewed learning from a complexity perspective as an emergent phenomenon, and proposes to compliment traditional quantitative and qualitative methodologies used in learning sciences research with computational agent-based modeling methods. The fourth paper is a "theoretical case study" in which an "ontological network theory" based on scale free networks is proposed, and then used to reframe the debate in the learning sciences concerning "coherent knowledge" versus "knowledge-in-pieces" theories of conceptual change. Overall, it is hoped this session stimulated interest in new theoretical and methodological "lenses" for understanding the challenges of learning about complex systems and for doing research into learning as complex systems.