1. I - Introduction

Umit Aslan, Nick LaGrassa
90-100 minutes
High School


This lesson introduces the students a real world fixed-volume container with gas: an air duster can. An explanation of the contents of the air duster can requires an understanding of gas particle behavior, which students will develop throughout the unit. The students are asked to generate three types of explanations about the can: (1) a text-based explanation, (2) a sketch illustration, and (3) a computational model. They develop the third explanation in three sub-steps. First, they build a static representation of the gas particle system using a NetLogo particle sandbox model. Then, they define the behavior of gas particles (e.g., their movement, collisions) using a blocks-based modeling toolkit. In this step, they work with only a few particles. Lastly, they bring in their gas behaviors to the particle sandbox and make their static model dynamic.

Throughout this lesson, they learn the rules that govern their behaviors and interactions by adding the rules into the model one-by-one. While observing the consequences of “running” these rules and the resulting motion of the particles. In addition, gain a familiarity with a microscopic view of the system and with the NetLogo model interface they will use again in later activities. This familiarity is a critical learning goal in the first Activity, since the use of computer interface (buttons, sliders, switches, etc...) becomes progressively more sophisticated in future activities. Finally, students reflect on their models and modeling practices.

Underlying Pages


Next Generation Science Standards
  • Physical Science
    • [HS-PS2] Motion and Stability: Forces and Interactions
  • NGSS Crosscutting Concept
    • Patterns
    • Systems
    • Structure and Function
  • NGSS Practice
    • Analyzing Data
    • Constructing Explanations, Designing Solutions
    • Asking Questions, Defining Problems
    • Using Models
    • Arguing from Evidence
    • Conducting Investigations
Computational Thinking in STEM
  • Data Practices
    • Analyzing Data
    • Collecting Data
    • Creating Data
    • Manipulating Data
    • Visualizing Data
  • Modeling and Simulation Practices
    • Assessing Computational Models
    • Designing Computational Models
    • Using Computational Models to Find and Test Solutions
    • Using Computational Models to Understand a Concept
  • Computational Problem Solving Practices
    • Assessing Different Approaches/Solutions to a Problem
    • Computer Programming
    • Troubleshooting and Debugging
  • Systems Thinking Practices
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System





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