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1. Capturing modeling pathways using the Modeling Assessment for Physics Laboratory Experiments

   https://doi.org/10.1119/perc.2020.pr.fox  

   Fox, Michael F.; Pollard, Benjamin; Ríos, Laura; Lewandowski, H. J. (September 2020, 2020 Physics Education Research Conference Proceedings)

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   A choose-your-own-adventure online assessment has been developed to measure the process of modeling undertaken by students when asked to measure the Earth's gravitational constant, g, using a simple pendulum. This activity forms part of the Modeling Assessment for Physics Laboratory Experiments (MAPLE), which is being developed to assess upper-division students' proficiency in modeling. The role of the pendulum activity is to serve as a pre-test assessment with apparatus that students are likely to be familiar. Using an initial sample of student data from a development phase of the assessment, we show that the pendulum activity is able to discriminate between a range of student processes that are relevant to understanding student engagement with modeling as a scientific tool. 

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2. The Modeling space : An Analytic tool for documenting students’ modeling activities

   Czocher, J. A.; Hardison, H. (February 2020, Proceedings of the 23rd Annual Conference on Research in Undergraduate Mathematics Education)
3. Analogies for modeling belief dynamics

   https://doi.org/10.1016/j.tics.2024.07.001  

   Olsson, Henrik; Galesic, Mirta (October 2024, Trends in Cognitive Sciences)

   Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development. 

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4. Programmatic modeling for biological systems

   https://doi.org/10.1016/j.coisb.2021.05.004  

   Lubbock, Alexander L.R.; Lopez, Carlos F. (September 2021, Current Opinion in Systems Biology)
5. Modeling Strategy for EMI Filters

   https://doi.org/10.1109/TEMC.2020.3006127  

   He, Ruijie; Xu, Yang; Walunj, Sameer; Yong, Shaohui; Khilkevich, Victor; Pommerenke, David; Aichele, Hermann L.; Boettcher, Martin; Hillenbrand, Philipp; Klaedtke, Andreas (January 2020, IEEE Transactions on Electromagnetic Compatibility)

   null (Ed.)