Teaching Lesson 3

In this lesson, it is important to support students as they modify the Rabbits and Grass model BUT not to give them the answers when they encounter a problems in their programming.  Students who think through their problem and come up with their own solutions are gaining independence as learners.

In the second activity, students will design and run experiments to see if adding a predator has an impact on the ecosystem. This activity will reinforce the concepts of energy flow through ecosystems and the often unexpected results of interactions in complex adaptive systems.

Review the activities from Lesson 3 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 3.

Lesson Objectives

The student will:

- Modify a simple computer model

- Learn CS concepts of user-defined variables and subclasses or breeds.

- Practice Pair Programming and Iterative design, implement, test cycle.

- Design and conduct an experiment

- Collect and analyze data to look for patterns

Teaching Summary

Getting started – 5 minutes

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


                  Activity #1: Adding a predator – 20 minutes

2.     New Concepts: adding breeds and setting user-defined traits

3.     Testing your model


                  Activity #2: Designing and running experiments – 20 minutes

4.     Designing your experiment

5.     Running your experiment

6.     Collecting and analyzing data


                  Wrap-up – 5 minutes

7.     In the real world, what might impact how animals use and gain energy? 

8.     Was your prediction about how adding a predator would impact the ecosystem correct? Why or why not?

9.     How can computer models be useful in understanding ecosystems?

Assessment questions (suggested):

      How would you compare the health of the ecosystem with and without a predator?

      What was the impact of adding a predator?

      What is an example of how an IF/THEN was used in this model?

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.


MS-LS2-4. Construct an argument supported by empirical evidence that changes to physical or biological components of an ecosystem affect populations.

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 groups that are underrepresented 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 3: Modifying the Model, Adding a predator

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


(EDS) We recommend acknowledging different cultures’ relationship and perception of top predators and making clear that they are not either Bad or Good.


(URG) Students are given “agency” as the creators of their own models, and as researchers seeking to answer a question or understand a phenomenon. The models that students build are their own creations.


(DIS)(GAT) There is ample opportunity to extend level of content and time for practice by compacting areas already mastered and to allow more time for students to complete previous experiments and customizations.  This differentiation strategy of pacing can benefit a wide range of students including those with learning disabilities and/or gifted and talented students.