Learning Crosscutting Scientific Concepts Through Swarming Behavior and Agent-Based Modeling
The goal of this project is to help students learn complex systems concepts that cut across multiple scientific disciplines in order to provide students with organizing frameworks for science learning. The study of complex systems is an emerging field of science on how interaction among numerous parts of the system gives rise to the overall behavior of the system. Many traditionally segregated science subjects in schools can be unified if taken as complex systems. Complex systems concepts, such as multi-agent, positive feedback, and decentralized control are powerful ideas to understand complex systems across domains. However, these concepts have been shown to be difficult to learn. We achieve this goal by combining Next Generation Science Standard (NGSS) style inquiry process with NetLogo simulation of honeybees' hive-finding behavior--a rich and intriguing phenomenon that embodies multiple complex systems concepts.
NGSS calls for students' meaningful use of scientific practices to develop and evaluate explanatory models of phenomena. BeeSmart revolves around a fun and intriguing phenomenon--a swarm of ten thousand bees can accurately choose the single best hive site from dozens of potential sites available without any leader or coordinator. This phenomenon vividly demonstrates multiple complex systems concepts in action. Presented with NetLogo agent-based models that are based on decades of research on swarm intelligence, students are driven by their curiosity to observe the phenomenon, raise their own questions about the phenomenon, generate hypotheses, verify their hypotheses by manipulating the models, discover the mechanism, and understand the underlying concepts.
BeeSmart is based on Seeley's Honeybee Democracy (2010) (pictured)
BeeSmart Hive-Finding Model is included in the Models Library of NetLogo Version 5.2 and later. For more details about this model or to download it, click Download BeeSmart.
Yu (Bryan) Guo and Uri Wilensky
Guo, Y., & Wilensky, U. (2014) Beesmart: a microworld for swarming behavior and for learning complex systems concepts. Proceedings of the Constructionism 2014 Conference. Vienna, Austria. August 2014.
Guo, Y. and Wilensky, U. (2014). NetLogo BeeSmart - Hive Finding model. http://ccl.northwestern.edu/netlogo/models/BeeSmart-HiveFinding. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.