Lesson 1. Intro to Learning with Computational Models

Sugat Dabholkar, Connor Bain, Philip Woods, Kevin Hall
Environmental Science
40-50 min
High School Advanced Biology (AP)
v4

Overview

This is an introductory lesson for using certain types of computational models designed using a software called NetLogo.

In this lesson, students will learn -

  • how to computationally study the spread of wildfire
  • how to engage in the scientific inquiry practices of constructing knowledge in the context of an Emergent Systems Microworld (ESM)
  • how to engage in computational thinking practices in the context of an ESM.

We will focus on four computational thinking practices: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices.

Standards

Next Generation Science Standards
  • Life Science
    • [HS-LS2] Ecosystems: Interactions, Energy, and Dynamics
    • [HS-LS4] Biological Evolution: Unity and Diversity
  • NGSS Crosscutting Concept
    • Patterns
    • Systems
    • Stability and Change
  • NGSS Practice
    • Analyzing Data
    • Using Models
    • Conducting Investigations
Computational Thinking in STEM
  • Data Practices
    • Analyzing Data
    • Manipulating Data
    • Visualizing Data
  • Modeling and Simulation Practices
    • Using Computational Models to Find and Test Solutions
    • Using Computational Models to Understand a Concept
  • Computational Problem Solving Practices
    • Troubleshooting and Debugging
  • Systems Thinking Practices
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System

Credits

Unit co-designed by Sugat Dabholkar in consultation with teachers at Schurz High School

Acknowledgement

CODAP is a computational tool for data analysis and representation developed and built by The Concord Consortium at https://codap.concord.org/  

The first four lessons are based on a Howard Hughes Medical Institute (HHMI) Biointeractive (https://www.hhmi.org/biointeractive/pocket-mouse-evolution)

Lesson 5 is based on the lesson Evolution in Action: The Galápagos Finches Authored by Paul Strode for Howard Hughes Medical Institute based on data collected by Peter and Rosemary Grant, Princeton University.

This work is supported by the National Science Foundation (grants CNS-1138461, CNS-1441041 and DRL-1020101) and the Spencer Foundation (grant 201600069). Any opinions, findings, conclusions, and/or recommendations are those of the investigators and do not necessarily reflect the views of the funding organizations.

Activities

  • 1. Using models to learn science
  • 2. Systematically investigating the spread of a forest fire

Student Directions and Resources


Several lessons in this curriculum use computational models designed using a piece of software called NetLogo. In this lesson, we will try to understand what computational models are and how to use them.

This lesson specifically focuses on learning science with computational models of emergent natural phenomena. Emergent phenomena are ones in which simple interactions between agents and their environment result in complex patterns. For example, a flock of birds (see below). 

                    

In a flock of birds, most people assume that the "head" bird is a leader of the flock. However, flocks actually emerge from each bird following a simple set of rules regarding how close and how far they should be from their neighbors as well as general direction of travel. This means that the shape of a flock is emergent and not directed by any particular leader bird.

We can use computational models to study and make predictions about emergent behaviors as long as we have a realistic understanding of the rules followed by the individual agents. 

Learning Goals - 

  • In this lesson, we will use a NetLogo model about Forest Fires to learn about how to computationally study a scientific phenomenon.
  • We will learn how to engage in the scientific inquiry practices of constructing knowledge.
  • We will learn how to engage computational thinking practices.

We will focus on four computational thinking practices: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices.

Let's get started!

1. Using models to learn science


Scientists use scientific modeling approaches to construct knowledge about the world. In this section, we explore the ideas behind scientific models.

 


Question 1.1

It only became widely accepted knowledge that all matter in the world is made up of tiny elementary particles in the early 19th century.

Let's look at the the picture below. What do you think this image is a model of?



Question 1.2

Some of you probably said it's a model of an atom. Others might say it's a model of a 'Neon atom', because it has 10 electrons. In fact, since we don't know the number of protons, it could be an ion of a different element!

The point is that these representations in a model allow us to think about natural phenomena (like atoms containing electrons) that are associated with the model in certain way. Can you think of what this particular model could be useful for?



Question 1.3

Now, let's look at a computational model of a forest. Imagine that you have a drone with a camera that is hovering above a forest. In other words, this model shows a top-down view of a forest. Each green patch you see represents a tree. A red patch represents a burning tree. 

Play with the model and make some observations.

To run the model, make sure that you press 'setup' before you press 'go'.

What do you think a researcher or scientist could use this model for?



Question 1.4

Make sure to change the density of trees in the model and observe the spread of the fire.

In this model, trees are called agents because their behaviors are programed into the model using a set of rules. 

An example of one such rule is a tree cannot move. Another is when a tree is on fire it turns red. All trees follow the same set of rules.

How might you write a rule that a tree could follow that describes how they catch on fire?



Question 1.5

Based on your exploration of the model, can you guess how the density of trees affects the spread of the fire in the forest?



Question 1.6

This 'fire model' is a computational model, used to study how the interactions between the agents (trees) allows us to observe and understand emergent patterns like the spread of fire in the forest. Because it is a computational model, we can easily change parameters/variables such as the density of trees and then study how that change affects the spread of fire in the forest. Although this is just a model, we can use that knowledge to make predictions regarding the spread of fire in a real forest.

However, this model does not include all the factors that affect the spread of fire in a real forest. Brainstorm several other factors that might affect the spread of a real forest fire that could be added to this model?

2. Systematically investigating the spread of a forest fire


Let's investigate how the density of the trees affects the spread of a forest fire. We will first generate some data using the Fire model and then visualize that data using another computational tool called CODAP.

Follow the experimental design that is described below:

Research Question: How does the density of trees in a forest affect the spread of a forest fire?

Hypothesis: As the density of trees in the forest increases, the percentage of forest burned will increase "linearly". (That means, if density of trees doubles, the percentage of forest burned will also double)

Let's test our hypothesis using the model.

Change the values of density systematically (plan out a series of different values to try). Record the value of 'percentage burned' in the data table for each density. Make sure that you press the 'setup' button every time you do a trial. Make sure to run each different value of density twice (2 trials) and finally, make sure you record values for each experimental trial. 

CODAP will automatically graph the average of the two trials that you will record. 

  


Question 2.1

Write some observations about the graph of 'density%' vs 'percent burned average'.



Question 2.2

Do you think that the evidence that we gathered with our experiment supports our hypothesis?



Question 2.3

Explain your answer to the previous question.



Question 2.4

The spread of a forest fire is an emergent phenomenon. Below a certain density, the fire does not spread much, however when the density crosses a 'tipping point' or threshold, the fire engulfs almost the whole forest. 

The tipping point in this model falls within which of the following density ranges?

  Between 30 and 40
  Between 40 and 50
  Between 50 and 60
  Between 60 and 70


Question 2.5

Can you give an example of another phenomenon with a tipping point?