Complex systems training in biology using an agent-based modeling approach

Background/Question/Methods

Biology instruction at the secondary and undergraduate level is undergoing rapid change. Some of these changes are in response to a growing recognition that students are not leaving their coursework in biology with the conceptual understanding, skill set, or habits of mind that we associate with proficiency in biology. While there are a number of potential explanations for these deficiencies, one may be that students often lack an understanding of complex systems. Complex systems exhibit emergent and non-intuitive properties and processes that cannot be predicted from the behavior of their component parts. Some example concepts associated with complex systems include randomness, non-linear responses, and feedback loops. Given the ubiquity of complex systems in biology, mental models that incorporate aspects of complex systems may better support student learning in biology. However, there has been little in the way of development of instructional approaches to address these challenges. This talk discusses one portion of BioGraph, an NSF-funded multi-year collaborative project to restructure high school biology instruction within a complex systems framework. This project includes a needs assessment, alignment of biology concepts with complex systems concepts, curricular design, professional development, and assessment of effectiveness. In this talk I will focus only on curricular design and assessment.

Results/Conclusions

We are developing curricular modules for use in three units of 9^th grade biology: chemistry of life, ecology, and evolution. Each module focuses on a specific set of biological and complex systems concepts, and includes teacher guides and student worksheets in support of a central activity. These activities make use of StarLogo TNG, an agent-based modeling and simulation software package. Students help program simulations of biological systems, run experiments, and analyze the data that they collect. Preliminary results show students gains in biology and complex systems understanding after completion of one of the two modules discussed in this talk. We also developed a short survey and coding rubric to assess changes in student biology and complex systems understanding across the school year. We are scoring complex systems understanding at three levels across four categories: predictability, processes, order, and emergence and scale. Preliminary results suggest that students have more difficulty with predictability than with the other categories. As work continues on this project we hope to provide greater insight into the importance of complex systems understanding in biology and to provide useful instructional tools for supporting biology learning at the high school level.