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1. Impact of a Blended Immersive and Computational Modeling Tool on Elementary Ecosystems Science Learning

   Metcalf, S.J.; Dickes, A.C.; Reilly, J.M.; Kamarainen, A.M.; Brennan, K.A.; Grotzer, T.A.; Dede, C.J. (January 2020, Annual meeting program American Educational Research Association)

   null (Ed.)

   Integrating computational thinking and scientific modeling in elementary school is challenging, but provides opportunities to meet important 21st century learning goals. Furthermore, computational modeling deepens the level of understanding of the modeling process and the phenomena being modeled, making science content more accessible. This paper presents findings from EcoMOD, a research project that blends an immersive virtual ecosystem and a 2D modeling environment to support computational modeling and ecosystem science learning in 3rd grade. As part of the study, students filled out pre- and post- surveys about science content understanding, affective measures, and scientific modeling. Findings for the overall student gains across the three dimensions of the surveys suggest that the EcoMOD curriculum was effective in learning ecosystem science content and practice, as well as developing an understanding of the value of computational modeling. 

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2. The effect of using different computational system modeling approaches on applying systems thinking

   https://doi.org/10.3389/feduc.2023.1173792  

   Eidin, Emil; Bowers, Jonathan; Damelin, Dan; Krajcik, Joe (June 2023, Frontiers in Education)

   This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patterns, and equilibrium. In contrast, ST has shown promise as an approach for making sense of complex problems. To facilitate ST, computational modeling tools have been developed, but it is not clear to what extent different approaches promote specific aspects of ST and whether scaffolding such thinking should start with supporting students first in linear causal reasoning before moving to more complex causal dimensions. This study compares two computational modeling approaches, static equilibrium and system dynamics modeling, and their potential to engage students in applying ST aspects in their explanations of the evaporative cooling phenomenon. To make such a comparison we analyzed 10th grade chemistry students’ explanations of the phenomenon as they constructed and used both modeling approaches. The findings suggest that using a system dynamics approach prompts more complex reasoning aligning with ST aspects. However, some students remain resistant to the application of ST and continue to favor linear causal explanations with both modeling approaches. This study provides evidence for the potential of using system dynamics models in applying ST. In addition, the results raise questions about whether linear causal reasoning may serve as a scaffold for engaging students in more sophisticated types of reasoning. 

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3. Integrating interactive computer simulations into K-12 earth and environmental science

   https://doi.org/10.1109/ISECon.2018.8340488  

   Zhu, Michelle; Panorkou, Nicole; Lal, Pankaj; Etikyala, Sowmith; Germia, Erell; Iranah, Pricilla; Samanthula, Bharath; Basu, Debasmita (March 2018, Proceedings of IEEE Integrated STEM education Conference (ISEC) 2018)

   This paper discusses our work in progress aiming to explore how computer simulations can be integrated into the K-12 curriculum of Earth and Environmental science. Several interactive simulations using Netlogo, a multi-agent modeling environment, and Scratch, a visual programming software are being developed with steerable parameters and the corresponding output plots for students to manipulate and interpret the results, respectively. Here, we present two simulations we designed on water cycle and discuss how these may help students learn about the distribution of water and its continuous move in the ecosystem. 

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4. Making Sense of Models: Connecting Science and Math Through Decoding and Modifying Computational Models

   Lee, I; Sagartz, M; Meyer, P; Anderson, E (April 2025, Science scope)

   This practitioner paper describes the Making Sense of Models (MSM) curriculum that bridges math and science learning through agent based modeling and rich computational thinking investigations that do not require teaching computer programming in middle school classrooms. The MSM curriculum supports students in the NGSS skill of reasoning about how and why a phenomenon happens. After developing decoding skills, students are able to assess the validity of a model based on comparing mechanisms in the model to what they learned about the phenomenon being modeled. The paper also describes how the MSM curriculum supports students’ ability to reason about scientific models and the real world. 

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5. Preparing the Next Generation of Integrative Organismal Biologists

   https://doi.org/10.1093/icb/icae098  

   Padilla, Dianna_K; Grünbaum, Daniel (July 2024, Integrative And Comparative Biology)

   Synopsis Pursuing cutting edge questions in organismal biology in the future will require novel approaches for training the next generation of organismal biologists, including knowledge and use of systems-type modeling combined with integrative organismal biology. We link agendas recommending changes in science education and practice across three levels: Broadening the concept of organismal biology to promote modeling organisms as systems interacting with higher and lower organizational levels; enhancing undergraduate science education to improve applications of quantitative reasoning and modeling in the scientific process; and K-12 curricula based on Next Generation Science Standards emphasizing development and use of models in the context of explanatory science, solution design, and evaluating and communicating information. Out of each of these initiatives emerges an emphasis on routine use of models as tools for hypothesis testing and prediction. The question remains, however, what is the best approach for training the next generation of organismal biology students to facilitate their understanding and use of models? We address this question by proposing new ways of teaching and learning, including the development of interactive web-based modeling modules that lower barriers for scientists approaching this new way of imagining and conducting integrative organismal biology. 

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