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We are inundated with models almost hourly today, and most seem to relate to the coronavirus. Here are just a few sentences I plucked off the web from very recent posts:

The “flatten the curve graph represents estimates produced by models. These models simulate the number of people who would be infected, require hospitalization, or die under different conditions.”

“Epidemiologists routinely turn to models to predict the progression of an infectious disease.”

“An influential coronavirus model … predicts the pandemic will “peter out” in May, but experts are wary…”

Wow! Models are not new in science or science education. Let’s take a look at a bit of history and then some examples.

In the 1996 National Science Education Standards, models were characterized as one of the “unifying concepts and practices” of science, which was not a trivial matter. Because models were one of many unifying processes, they were doomed to a nod, a wink and then, for the most part, ignored. Everyone said—sure, we do models and modeling. But were we really?

NGSS – Developing and Using Models

Travel ahead to the Next Generation Science Standards (NGSS) released in the last few years and all of a sudden models and modeling have taken on new importance. They are listed as one of eight scientific and engineering practices (Developing and using models) with all the practices taking on a top priority in the standards by being the central action in performance expectations. So instead of models being one of several unifying processes, it now is taking a front-row seat. In addition, it is not simply about understanding models, as was implied in the 1996 standards, but about developing and using these models. Students are now being asked to do something with them—a much more difficult task.

That alone does not account for the new prominence of models. So, what is happening and why the shift? I believe the major reason is that models and modeling today are a part of our everyday lives, like never before. Witness their importance in predicting COVID-19 occurrence. Let’s investigate some other examples.

I live in the southeastern part of the United States. We get hurricanes regularly in the late summer and early fall. When they occur, the weather forecasters on TV and the internet usually go crazy. There is “good science” behind their obsessions—modeling. Have you seen the ‘spaghetti diagrams’ from the Weather Channel? I am sure you have, but just in case here’s is an example:

The ‘spaghetti diagram’ are the colored lines that represent the different predictions or models for what path the storm might take. This very important information indicates both the power and uncertainty of models. They are the best predictions, based on the available data for what might happen. They are also fallible. Not every spaghetti line can become a reality.

Let’s look at another example – following your favorite sports team on the Internet. Sometimes my football team is not available on TV (I know this is hard to believe) and I have to follow the game on ESPN. What I get is a clunky football field and a play-by-play listing of what happens. More importantly, I also have a prediction of who is going to win the game at any given moment, something that looks like this:


Not surprisingly, this is also a model of a future outcome based on data.

Where else do we use models? In terms of people, we speak of fashion and role models. In business, we talk of business models, or product models. Some of these represent things—product models—and others are more scientific like economic models in which reams of data are used to predict future trends. Not all models are used to predict. Some merely represent a phenomenon, e.g., a model car or a model of a tree taproot. These are physical replicas of real-world phenomena. The more powerful models do predict and thus help us solve problems, such as the COVID-19 and hurricane track models.

In science, we use models regularly. The simple Punnett square is a model of what offspring to expect if you cross one individual with another. Predictions of eclipses or the motion of planets and comets are also models. So are predictions of crop and harvest data in agriculture. In fact, models are all around us.

Using Models in K-12 Science Education

So, what does this mean for science education? In the early elementary grades, students might create a physical model—of a flower or of the planets. They may use models to make predictions based on their investigations. In middle grades, students are asked to take it up a notch by representing complex systems like interactions in an aquatic ecosystem or in a physical model of the cell. In high school, it gets even more complex representing the processes of photosynthesis and cellular respiration.

In all cases, however, students must be able to explain their models. In addition, they should show how the model is representative of the phenomenon and how it is different from the real process. This is the key—being able to create, describe and understand a model. As teachers, it is critical that we hold students accountable for all aspects of modeling. It is also important that we call out instances when students are using models—whether it be when analyzing offspring through a Punnett square or interpreting diagrams of radioactive decay.

While the coronavirus outbreak has totally disrupted our lives, it also provides an opportunity for students to better understand the role and nature of models. Models rely on data to be reliable. The more accurate the data the more reliable the model becomes. You might have students investigate other epidemiological models, such as the 1854 cholera outbreak in London. Explore the maps (really models) created by John Snow and how they show that drinking water was the cause of the outbreak. Have them identify the factors underlying todays ‘flatten the curve’ effort. Or, have them search for examples of models that have been proven terribly wrong by doing an internet research. Have them explore what caused the model to be erroneous.

In closing, we are in a new era. Models are more and more a part of daily life. Let’s make sure students learn from, utilize, appreciate and understand their role. Also let’s make sure they are aware of the possible fallibility of these models. They are predictions, not certainties.

Happy modeling!

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Michael Padilla

Michael Padilla

Author & Professor of Science Education, University of Georgia, Athens

Note: Fresh Ideas for Teaching blog contributors have been compensated for sharing personal teaching experiences on our blog. The views and opinions expressed in this blog are those of the authors and do not necessarily reflect the official policy or position of any other agency, organization, employer or company.