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1. Developing the systems thinking and computational thinking identification tool

   Bowers, J.; Shin, N.; Brennan, L.; Eidin, E. E.; Stephens, L.; Roderick, S. (January 2022, Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022)

   Chinn, C.; Tan, E.; Chan, C.; Kali, Y. (Ed.)

   We developed the Systems Thinking (ST) and Computational Thinking (CT) Identification Tool (ID Tool) to identify student involvement in ST and CT as they construct and revise computational models. Our ID Tool builds off the ST and CT Through Modeling Framework, emphasizing the synergistic relationship between ST and CT and demonstrating how both can be supported through computational modeling. This paper describes the process of designing and validating the ID Tool with special emphasis on the observable indicators of testing and debugging computational models. We collected 75 hours of students’ interactions with a computational modeling tool and analyzed them using the ID Tool to characterize students’ use of ST and CT when involved in modeling. The results suggest that the ID Tool has the potential to allow researchers and practitioners to identify student involvement in various aspects of ST and CT as they construct and revise computational models. 

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2. How Fifth-Grade English Learners Engage in Systems Thinking Using Computational Models

   https://doi.org/10.3390/systems8040047  

   Haas, Alison; Grapin, Scott E.; Wendel, Daniel; Llosa, Lorena; Lee, Okhee (December 2020, Systems)

   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. 

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3. Designing heterogeneous hierarchical material systems: a holistic approach to structural and materials design

   https://doi.org/10.1557/mrc.2019.60  

   Ryan, Emily; Pollard, Zoe A.; Ha, Quang-Thinh; Roshandelpoor, Athar; Vakili, Pirooz; Goldfarb, Jillian L. (June 2019, MRS Communications)

   Many materials systems comprise complex structures where multiple materials are integrated to achieve a desired performance. Often in these systems, it is a combination of both the materials and their structure that dictate performance. Here the authors layout an integrated computational–statistical–experimental methodology for hierarchical materials systems that takes a holistic design approach to both the material and structure. The authors used computational modeling of the physical system combined with statistical design of experiments to explore an activated carbon adsorption bed. The large parameter space makes experimental optimization impractical. Instead, a computational–statistical approach is coupled with physical experiments to validate the optimization results. 

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4. A framework for supporting systems thinking and computational thinking through constructing models

   https://doi.org/10.1007/s11251-022-09590-9  

   Shin, Namsoo; Bowers, Jonathan; Roderick, Steve; McIntyre, Cynthia; Stephens, A. Lynn; Eidin, Emil; Krajcik, Joseph; Damelin, Daniel (July 2022, Instructional Science)

   Abstract We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling. 

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5. Key challenges facing data-driven multicellular systems biology

   https://doi.org/10.1093/gigascience/giz127  

   Macklin, Paul (October 2019, GigaScience)

   Abstract Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and financial) commitment. 

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