Understanding Complex Systems and Climate Change: Learning Designs With Agent-Based Models, Productive Failure, and Analogical Encoding

• In Event: 26.029 - Student Learning via New Tools and Approaches

Abstract

This paper reports on research in which students used agent-based computer models to learn about complex systems and climate change. The intervention in this study involved a set of problem-based learning activities used as part of a productive failure (PF) learning design. The experimental group used a hybrid approach that combined PF with analogical encoding theory, whereas the comparison group used a PF only treatment. Both groups made significant learning gains by the posttest and the complexity and climate change ideas, however, the experimental group performed at a significantly higher level on a posttest transfer problem that required the application of complexity ideas. Implications and directions for future research are discussed.

Authors

• Michael J. Jacobson, The University of Sydney

• Lina Markauskaite, The University of Sydney