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Historic challenges in the biological sciences, such as the spread of disease and climate change, have created an unprecedented need for humans to engage with scientific information to address societal problems. However, understanding these socioscientific issues (SSI) can be hard due to the difficulty of comprehending their complex structures and behaviors, the intentional propagation of misinformation, and an insufficient understanding of the epistemic practices that scientists use to develop relevant knowledge. Education researchers have highlighted additional problems in the way science is taught with a focus mainly on concepts rather than practices, competing curricular mandates, and professional development activities that do not provide usable knowledge. The research reported here follows more than a decade of work using agent-based computational models to support the comprehension and analysis of complex biological systems. Our recent work has aimed to build tools and strategies to support students in decision making about complex SSIs. In this paper, we discuss 7 design challenges and principles that underpin this recent focus. Specifically, we combine agent-based modeling with strategies to develop students’ epistemic performance in high school biology curricula. We then provide a detailed case study of how the 7 design principles were used to create a disease epidemic model and unit anchored in the biology topic of the nature of science. Our goal is to offer a comprehensive set of research-derived design principles that can bridge classroom experiences in biology to applications of SSIs. 

The purpose of this study was to investigate how computational modeling promotes systems thinking for English Learners (ELs) in fifth-grade science instruction. Individual student interviews were conducted with nine ELs about computational models of landfill bottle systems they had developed as part of a physical science unit. We found evidence of student engagement in four systems thinking practices. Students used data produced by their models to investigate the landfill bottle system as a whole (Practice 1). Students identified agents and their relationships in the system (Practice 2). Students thought in levels, shuttling between the agent and aggregate levels (Practice 3). However, while students could think in levels to develop their models, they struggled to engage in this practice when presented with novel scenarios (e.g., open vs. closed system). Finally, students communicated information about the system using multiple modalities and less-than-perfect English (Practice 4). Overall, these findings suggest that integrating computational modeling into standards-aligned science instruction can provide a rich context for fostering systems thinking among linguistically diverse elementary students.