By Scott E. Grapin, Daniel Wendel, Alison Haas, and Okhee Lee
Middle school students, as scientists, use computational modeling to explain how a virus such as COVID-19 spreads through a community. Specifically, students use a computational model to identify patterns in two conditions that contribute to the spread of the virus: (1) people’s mobility and (2) virus transmissibility. Then students consider the benefits and limitations of the computational model for explaining the phenomenon of viral spread.
Access the Lesson: How Can Computational Modeling Help Explain the Spread of COVID-19?
To make a copy of the lesson, follow the link to the Google doc and select File > Make a Copy.
This lesson is written for student use (e.g., “You will program the model…”). To provide support for students, the lesson includes response spaces and embedded scaffolds. Teachers and families are encouraged to adapt the lesson for their contexts (e.g., using provided guiding questions to facilitate discussions in the classroom or at home).
How Can Computational Modeling Help Explain the Spread of COVID-19? is a stand-alone task. However, this lesson can be taught as part of an instructional sequence in which students build toward using a computational model to design solutions to the disproportionate impact of COVID-19 on racial and ethnic minority groups. This is the first of three lessons in the Understanding COVID-19 Disparities Using Computational Modeling playlist.
*If students are new to the StarLogo Nova programming environment, they are encouraged to complete the StarLogo Nova Orientation Task (from the Computational Thinking and Modeling NSTA Playlist) prior to engaging in the lessons.
**How Can Computational Modeling Help Explain the Spread of COVID-19? is the first of three lessons on the Understanding COVID-19 Disparities Using Computational Modeling playlist.
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