Teaching Lesson 2

The most important take away in this lesson is that students can decipher models themselves and find out exactly how the model works and what is included in it.  This is a powerful realization that leads to greater independence as code readers and learners.  With this information, students will learn what variables they can control in an existing model (because it is in the code or user interface) to run experiments and learn about the system portrayed.

It may be important to review the elements of systematic experimentation with students before they approach designing their experiments. 

Review the activities from Lesson 2 as well as the material below. Reflect on how you would teach this in your class. Post your reflection to your portfolio in "Pedagogy->Module 3" under the heading Lesson 2.

Reflect on the Scientific Practices you've used with computer modeling and simulation. Upload your reflections in your portfolio in the "Reflections->Computer Modeling & Simulation section under the heading "Scientific Practices with Modeling and Simulation." >

Lesson Objectives

The student will:

- Decode a simple model of a complex adaptive system (LO7)

- Trace a program’s execution (LO8)

- Ask a question and design an experiment (LO9)

- Conduct an experiment using a computer model (LO10)

- Make observations (drawing simple correlations) (LO11)

Teaching Summary

Getting started – 5 minutes

1.     Review of the previous day’s lesson and concepts and connection to today’s lesson.


                  Activity #1: Looking under the hood – 20 minutes

2.     Familiar and New Command Blocks

3.     Decoding a model – looking for the parts and interactions between them

4.     What calls what? – execution of the program loop


                  Activity #2: Designing and running experiments – 20 minutes

5.     Experimental design

6.     Running experiments

7.     Collecting and analyzing data


                  Wrap-up – 5 minutes

8.     What does computer modeling and simulation allow us to do that would be difficult to do in the real world?

Assessment questions (suggested):

      Is the Rabbits and Grass ecosystem a complex adaptive system?  Why or why not?  (LO7)

      What rabbit procedures were called when the forever button was toggled on? (LO8)

      What were the independent and dependent variables in your experimental design (LO9)

      How many times did you have to run your model at each setting?  Why?  (LO10)

      Give an example of a correlation you observed after running experiments with the model (LO11)

NGSS Performance Expectations

Ecosystems: Interactions, Energy, and Dynamics

MS-LS2-1. Analyze and interpret data to provide evidence for the effects of resource availability on organisms and populations of organisms in an ecosystem.

NRC Disciplinary Core Ideas

Interdependent Relationships in Ecosystems

DCI-LS2.A: Organisms, and populations of organisms, are dependent on their environmental interactions both with other living things and with nonliving factors. In any ecosystem, organisms and populations with similar requirements for food, water, oxygen, or other resources may compete with each other for limited resources, access to which consequently constrains their growth and reproduction. Growth of organisms and population increases are limited by access to resources.


Ecosystem Dynamics, Functioning, and Resilience

DCI-LS2.C: Ecosystems are dynamic in nature; their characteristics can vary over time. Disruptions to any physical or biological component of an ecosystem can lead to shifts in all its populations.


NRC Scientific and Engineering Practice Standards

Practice 1: Asking questions and defining problems

1A: Ask questions that arise from careful observation of phenomena, models, or unexpected results.

1B: Ask question to identify and/or clarify evidence and/or the premise(s) of an argument.

1C: Ask questions to determine relationships between independent and dependent variables and relationships in models.


Practice 2: Developing and using models

2A: Evaluate limitations of a model for a proposed object or tool.

2C: Use and/or develop a model of simple systems with uncertain and less predictable factors.

2E: Develop and/or use a model to predict and/or describe phenomena.

2G: Develop and/or use a model to generate data to test ideas about phenomena in natural or designed systems, including those representing inputs and outputs, and those at unobservable scales.


Practice 3: Planning and carrying out investigations

3A: Plan an investigation individually and collaboratively, and in the design: identify independent and dependent variables and controls, what tools are needed to do the gathering, how measurements will be recorded, and how many data are needed to support a claim.

3B: Conduct an investigation and/or evaluate and/or revise the experimental design to produce data to serve as the basis for evidence that meet the goals of the investigation.

3D: Collect data to produce data to serve as the basis for evidence to answer scientific questions or test design solutions under a range of conditions.


Practice 4: Analyzing and interpreting data

4A: Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships.

4B: Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships.

4D: Analyze and interpret data to provide evidence for phenomena.


Practice 5: Using mathematics and computational thinking

5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions.

5D: Apply mathematical concepts and/or processes  (e.g., ratio, rate, percent, basic operations, simple algebra) to scientific and engineering questions and problems.


Practice 6: Constructing explanations and designing solutions

6A: Construct an explanation that includes qualitative or quantitative relationships between variables that predict(s) and/or describe(s) phenomena.

6B: Construct an explanation using models or representations.

6D: Apply scientific ideas, principles, and/or evidence to construct, revise and/or use an explanation for real-world phenomena, examples, or events.


Practice 7: Engaging in argument from evidence

7C: Construct, use, and/or present an oral and written argument supported by empirical evidence and scientific reasoning to support or refute an explanation or a model for a phenomenon or a solution to a problem.


Practice 8: Obtaining, evaluating, and communicating information

8E: Communicate scientific and/or technical information (e.g. about a proposed object, tool, process, system) in writing and/or through oral presentations.



NRC Crosscutting Concepts

1. Patterns:

1B: Patterns in rates of change and other numerical relationships can provide information about natural and human designed systems.

1D: Graphs, charts, and images can be used to identify patterns in data.


3. Scale, Proportion, and Quantity

3A: Time, space, and energy phenomena can be observed at various scales using models to study systems that are too large or too small.


4. Systems and Systems models

4A: Systems may interact with other systems; they may have sub-systems and be a part of larger complex systems.

4B: Models can be used to represent systems and their interactions—such as inputs, processes and outputs—and energy, matter, and information flows within systems.

4C: Models are limited in that they only represent certain aspects of the system under study.


5. Energy and Matter:

5B: Within a natural or designed system, the transfer of energy drives the motion and/or cycling of matter.


7. Stability and Change:

7A: Explanations of stability and change in natural or designed systems can be constructed by examining the changes over time and forces at different scales, including the atomic scale.

7C: Stability might be disturbed either by sudden events or gradual changes that accumulate over time.

7D: Systems in dynamic equilibrium are stable due to a balance of feedback mechanisms.




CSTA K-12 Computer Science Standards




Use abstraction to decompose a problem into sub problems.




Explain how sequence, selection, iteration and recursion are the building blocks of algorithms.


Connections to other fields


Provide examples of interdisciplinary applications of computational thinking.


Data representation


Describe how mathematical and statistical functions, sets, and logic are used in computation.


Modeling & simulation


Describe how a simulation can be used to solve a problem.


Modeling & simulation


Interact with content-specific models and simulations to support learning and research.


Modeling & simulation


Use modeling and simulation to represent and understand natural phenomena.


Modeling & simulation


Use models and simulation to help formulate, refine, and test scientific hypotheses.


Modeling & simulation


Analyze data and identify patterns through modeling and simulation.


Data collection & analysis


Collect and analyze data that are output from multiple runs of a computer program.


Data collection & analysis


Use data analysis to enhance understanding of complex natural and human systems.


Responsiveness to varied student learning needs:

In Project GUTS, we integrate teaching strategies found to be effective with learners with various backgrounds and characteristics such as economically disadvantaged students (EDS), students from underrepresented groups in STEM (URG) , students with disabilities (DIS), English Language learners (ELL), girls and young women (FEM), students in alternative education (ALT), and gifted and talented students (GAT).


In each lesson we describe the accommodations and differentiation strategies that are integrated in the activities to support a wide range of learners.


Module 3 Lesson 2: Rabbits and Grass model


(EDS)(URG)(ELL) We ask students to apply what they know, specific to their cultural and/or socio-economic context, when addressing issues related to ecosystems and the realism of models.  We can ask what is assumed in this model, what is realistic to the students, and what is not.


(URG)(DIS) We use technology to present information in multiple modes of representations. In the StarLogo Nova modeling and simulation environment students can present information as code blocks, text, graphical display of the simulation, and as data in tables and graphs.


(ELL) The “place-based” nature of this lesson establishes connections between school science and the students’ community and lives.


(FEM) Careful planning of partners for the on-computer activity is a strategy that encourages participation for the girls in science.


(FEM) (URG) We choose a curriculum topic, Ecosystems, that has relevancy and real-world application, to interest and engage the girls and students from underrepresented groups in STEM in the class.