Whether learning in a physical or virtual classroom, using models helps students understand real-world phenomena— like COVID-19—and solve problems related to phenomena.
- Scientists use models to help develop questions and explanations, generate data that can be used to make predictions, and communicate ideas to others (NSTA 2014).
- Engineers use models to help analyze a system to see where or under what conditions flaws might develop or to test possible solutions to a program (NSTA 2014).
The Progression of Modeling
- Through observations, testing, and refining, they use scientific models to explain phenomena and predict what may happen.
- To incorporate engineering practices, they create and use models to test solutions.
Model Growth by Grade Level
- Grades K–2: Builds on prior experiences and progresses to include using and developing models—such as diagrams, drawings, physical replicas, dioramas, dramatizations, and storyboards—that represent concrete events or design solutions.
- Grades 3–5: Builds on K–2 experiences and progresses to build and revise simple models and using models to represent events and design solutions.
- Grades 6–8: Builds on K–5 experiences and progresses to developing, using, and revising models to describe, test, and predict more abstract phenomena and design systems.
- Grades 9–12: Builds on K–8 experiences and progresses to using, synthesizing, and developing models to predict and show relationships among variables between systems and their components in the natural and designed worlds.
What Makes a Good Model?
No matter what type of model students use to explain a phenomenon, solve a problem, or make a prediction, the models should incorporate prior experiences to build toward answering a question or solving a problem about a phenomenon. Ideally, the question or problem should promote a transdisciplinary approach, integrating science content across a variety of subjects—such as literacy, history, art, and culture—giving the phenomena being investigated a real-world perspective. This can help students understand which features are important and how they interact, enabling them to not only develop explanations but also to use what they observe to make predictions.
As part of the process, students should acknowledge that their models are limited by the data known and be prepared to refine as new data becomes available. For example, in discussing pandemic models, scientists for the National Institute of Allergy and Infectious Diseases look at the data as it’s evolving and do everything they can to ignore extremes of the model. Atmospheric scientists who use computer models to forecast the path of a hurricane may have 20–30 different models that all differ slightly, so they need to examine trends. Soil scientists may need to simulate river flows in a variety of conditions to model real-world processes. Mechanical engineers may need to refine their models to better harness wind energy
O’Donnell says one example of a K–5 activity at ssec.si.edu/distancelearning that optimizes student learning by effectively incorporating modeling looks at shadows to examine the scientific phenomenon of the Sun’s apparent daily motion across the sky. Designed for grades K–3 students, the lesson builds toward answering an essential question: “Why is my shadow shorter sometimes and longer other times?”
Learning remotely with video support or in a classroom, students draw on prior experiences and share their ideas about the question, make and record observations, develop a model that will help them figure out the answer to the question, check the accuracy of their model through further observation, and make predictions.
What makes the activity an effective learning experience? O’Donnell summarizes: “It’s phenomenon driven, it creates a model to explain that phenomenon, and students engage in sensemaking and questioning, then revisit to understand that model.”
To assess students’ models, consider these points:
- Is the model based on reliable observations?
- Does it aid in sensemaking?
- Does it explain the characteristics of the observations used to formulate it?
- Is it predictive?
- Does it answer an essential question about a phenomenon or help students solve a problem?
- Can it be refined when new data is determined?
Modeling for the Real World
From the first months of the COVID-19 pandemic, phrases such as “flatten the curve” became a routine part of even casual conversations as the world population became invested in the science, evaluating epidemiology-driven models to make predictions and decisions that affected every aspect of society.
To prepare students for understanding phenomena they encounter, look for real-world current events that are examples of scientific modeling (e.g., predicting the spread of a pandemic, studying the path of a hurricane, reducing soil erosion on a hillside, or designing a wind turbine to harness wind energy). Create transdisciplinary science lessons that embed a universal perspective by incorporating a cultural view and ample opportunity to converge the history, art, and science of phenomena together. Lessons should engage even the youngest students in thinking critically as they seek to answer essential questions, creating a bank of prior knowledge that can be refined and applied to new and evolving situations.
An understanding of modeling not only provides a basis for students interested in future science technology, engineering, and math (STEM) careers but empowers all learners to be global citizens who are critical thinkers as they navigate phenomena in the world around them.
*Examples are from Smithsonian Science for the Classroom™.
REFERENCES
Alonso-Zaldivar, R., L. Neergaard, and The Associated Press. 2020. Fortune. “White House turns to statistical model of coronavirus forecast.” Accessed May 2020: https://fortune. com/2020/03/31/coronavirus-predictions-white-house-covid19-forecast/
Brown, R. April 8, 2020. The Conversation. “Scientific modelling is steering our response to coronavirus. But what is scientific modelling?” Accessed May 2020: https:// theconversation.com/scientific-modelling-is-steeringour-response-to-coronavirus-but-what-is-scientificmodelling-135938
Godden, D. R., and A. D. Baddeley. 1975. “Contextdependent memory in two natural environments: On land and underwater.” British Journal of Psychology, 66(3), 325–331. https://doi.org/10.1111/j.2044-8295.1975.tb01468
National Oceanic and Atmospheric Administration, US Department of Commerce. 2017. “6 Tools Our Meteorologists Use to Forecast the Weather.” Accessed May 2020: https:// www.noaa.gov/stories/6-tools-our-meteorologists-use-toforecast-weather
National Research Council. 2007. Taking Science to School: Learning and Teaching Science in Grades K–8. Washington, DC: The National Academies Press. https://doi. org/10.17226/11625
National Research Council. 2012. A Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Committee on Conceptual Framework for New K–12 Science Education Standards. Board on Science Education. Division of Behavioral and Social Sciences and Education. Washington DC: The National Academies Press.
National Science Teaching Association. 2014. Matrix of Science and Engineering Practices. Accessed May 2020: https://ngss.nsta.org/Practices.aspx?id=2
NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. “Appendix F—Scientific and Engineering Practices in NGSS.” Washington, DC: The National Academies Press.
O’Donnell, C. 2019. Parents League of New York. “STEM, STEAM: What Does It All Mean?” Accessed May 2020: https://www.parentsleague.org/blog/stem-steam-what-doesit-all-mean
Smithsonian Institution. 2020. Covid-19! How Can I Protect Myself and Others? Accessed May 2020: https://ssec.si.edu/covid-19
UCLA Samueli Newsroom. 2020. “UCLA Machine-Learning Model Is Helping CDC Predict Spread of COVID-19.” Accessed May 2020: https://samueli.ucla.edu/ucla-machinelearning-model-is-helping-cdc-predict-spread-of-covid-19/
How the Smithsonian Science Education Center Supports Using Models in Three-Dimensional Learning
The Smithsonian Science Education Center has developed Smithsonian Science for the Classroom™ for grades 1–5 and Science and Technology Concepts™ Middle School curricula from the ground up to engage students. Every module is three-dimensional, hands-on learning that incorporates science and engineering practices—including the modeling that helps students explain phenomena and engineer solutions to problems.
Each module offers opportunities to do science following a coherent progression as it integrates engineering concepts, literacy, and math, developing deep connections to phenomena. With print, digital, and lab materials in one all-inclusive package, the lessons are designed for classroom use but can be adapted to supplement distance learning. The accompanying literacy series, Smithsonian Science Stories, provides students with the opportunity to connect STEM to history, art, and culture at the point of use. The curricula help students realize how modeling can enhance understanding of realworld phenomena, improving the lives of all.
The Smithsonian Science Education Center also offers free STEM resources to support distance learning for grades K–8 at ssec.si.edu/distancelearning. Its most recently released resource is COVID-19! How Can I Protect Myself and Others? This free guide for youth helps students ages 8–17 understand the science (and social science) of the virus that causes COVID-19. Find it at ssec.si.edu/covid-19.
Learn more about Smithsonian Science for the Classroom:
Carolina Biological Supply Company. www.carolina.com/ssftc
Email: curriculum@carolina.com
Call: 800.334.5551
©Smithsonian Science Education Center. Transforming K–12 Education through Science ™ in collaboration with communities across the globe ScienceEducation.si.edu


