Modeling Ecosystems Computationally - Camelot - Excel Hs - Preview

Modeling Ecosystems Computationally - Camelot - Excel Hs

Subject: Biology
Time: 5-7 classes, 45-50 min each

Introductory High School Biology

Unit Overview

Students will develop an understanding of how to develop a computational model starting from basic intuitions about individual behavior, then gradually and iteratively making improvements and adding complexity.  They will begin by using block-based programming tools, but will eventually transition to simple text-based programming.  Finally, they will learn how to critically evaluate models for their realism and usefulness using data from the Isle Royale ecosystem, and use the model they built over the unit to make predictions about the future of that ecosystem.

Specifically, this unit uses the NetTango block-based programming interface and the NetLogo programming language for many of the lessons.  These tools will help facilitate discovery of important concepts in ecology and modeling including basic predator-prey population dynamics, evaluating models, and predicting ecological impacts.

Lessons Overview

1. Lesson 1 - Using Blocks to Model Ecosystems Overview

This unit introduces computational thinking practices which include data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. These practices are introduced to students in the context of a biology unit of ecology and evolution. 

2. Lesson 2 - Analyzing the Code Behind the Blocks Overview

In this lesson students explore and learn the text-based (NetLogo) code behind the coding NetTango blocks. 

4. Lesson 4 - Evaluating the Model Overview

In previous lessons in this unit, students have built up a model of a simple ecosystem based mainly on their intuitions about how individual animals would behave.  In this lesson they will use this model in several ways.  First, they will look at how that model is able to produce interesting patterns at the population level, even though this was not an intentional part of the process of building the model.  Second, they will think about what it means for a model to be good, and see how they can evaluate their model by comparing its results to real data from Isle Royale.

5. Lesson 5 - Using the Model to Make Predictions Overview

A major point of the previous lesson was to show students how to evaluate whether a model is a good one.  They should have found that their model is pretty good at matching what scientists see on Isle Royale.  Based on this confidence in the accuracy of their model, they will now use it to make predictions about the future of Isle Royale and the effects of different potential conservation efforts.

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Next Generation Science Standards
  • Life Science
    • [HS-LS4] Biological Evolution: Unity and Diversity
    • [HS-LS2] Ecosystems: Interactions, Energy, and Dynamics
  • NGSS Crosscutting Concept
    • Patterns
    • Systems
    • Stability and Change
  • NGSS Practice
    • Using Models
    • Conducting Investigations
    • Analyzing Data

Computational Thinking in STEM
  • 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
  • Data Practices
    • Analyzing Data
    • Manipulating Data
    • Visualizing Data
  • Systems Thinking Practices
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System


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.

Comments, Feedback, and Questions


Computational Thinking in Science and Math

Lesson 4 - Evaluating the Model

In this lesson, you will look at a version of the model you have been building in the earlier lessons. You will evaluate the accuracy of the model based on data from the real world.