# CT-STEM

The Physics Of Angry Birds - Preview

# The Physics Of Angry Birds

Subject: Physics
Time: 2-3 class periods (45 minutes each)
Level:

Physics; best-suited for students who have had pre-calculus and a basic understanding of Newton's laws.

## Lesson Overview

The activity has two main parts: 1.) Gathering data and 2.) Analyzing data and forming models/equations. The students will use computational tools to capture the motion of an Angry Bird in its world, record the data, determine the mathematical functional form of the data, and ultimately derive the gravitational acceleration on the Angry Birds’ world.

Student Outcomes

Learner Objectives:

1. Students are able to apply the equations of motion in a new context.

2. Students are able to use a computer simulation to generate data

3. Students are able to use a spreadsheet program to fit a mathematical model to their data.

4. Students understand the impact of short versus long time steps in a computer simulation.

1. Applications of learning: The students apply their knowledge of Newton's mechanics in solving a novel problem with a method that few, if any, will have used before.

2. Communicating: The students are instructed to generate a graph clearly showing their results.

3. Using technology: The students learn to use a program to fit their data to a model.

4. Working on teams: We encouraged working together in groups.

5. Making connections: We hope to promote the use of computer simulations in attacking physics problems rather than just as a tool for visualizing the data.

Prerequisites

- Students should be familiar with the equations of motion and how to apply them to projectiles.

- Familiarity with spreadsheets and organizing scientific data will come in handy when graphing and interpreting the results of the simulation.

mac

windows

laptops

chrome books

phones

tablets

## Standards

Next Generation Science Standards
• Physical Science
• [HS-PS2] Motion and Stability: Forces and Interactions

Computational Thinking in STEM
• Modeling and Simulation Practices
• Assessing Computational Models
• Computational Problem Solving Practices
• Choosing Effective Computational Tools
• Data Practices
• Analyzing Data
• Collecting Data
• Creating Data