Teaching Lesson 4
In this lesson, students design their own Greenhouse Gas projects consisting of a question, experimental design and model. In the first activity, students will learn about computational science and how to design a model, and will use this knowledge to scope their project. This leads to a second activity, in which they start designing and implementing their model, using the Greenhouse Gas base model as a starting place.
Assignment
Review the activities from Lesson 4 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 2" under the heading Lesson 4.
Lesson Objectives
Teaching Summary
Getting Started – 5 minutes
1. Review of the previous day’s lesson and concepts and connection to today’s lesson
Activity #1: Computational Science and Model Design – 20 minutes
2. Introduce key ingredients of computational science model design
3. Define your computational science project
Activity #2: Design and develop your model – 20 minutes
4. Agents and environment
5. Interactions
Wrap-Up – 5 minutes
6. What research is necessary to ground your model in reality?
7. How will you check to see if your model is realistic?
Assessment questions
Earth and Human Activity
MS-ESS3-3.Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.
MS-ESS3-4. Construct an argument supported by evidence for how increases in human population and per-capita consumption of natural resources impact Earth’s systems.
MS-ESS3-5. Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past century.
NRC Disciplinary Core Ideas
ESS3.C. Human Impacts on Earth Systems
Human activities have significantly altered the biosphere, sometimes damaging or destroying natural habitats and causing the extinction of other species. But changes to Earth’s environments can have different impacts (negative and positive) for different living things.
Typically as human populations and per-capita consumption of natural resources increase, so do the negative impacts on Earth unless the activities and technologies involved are engineered otherwise.
ESS3.D. Global Climate Change
Human activities, such as the release of greenhouse gases from burning fossil fuels, are major factors in the current rise in Earth’s mean surface temperature (global warming). Reducing the level of climate change and reducing human vulnerability to whatever climate changes do occur depend on the understanding of climate science, engineering capabilities, and other kinds of knowledge, such as understanding of human behavior and on applying that knowledge wisely in decisions and activities.
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.
1D: Ask questions to clarify and/or refine a model, an explanation, or an engineering problem.
1E: Ask questions that require sufficient and appropriate empirical evidence to answer.
Practice 2: Developing and using models
2A: Evaluate limitations of a model for a proposed object or tool.
2B: Develop or modify a model—based on evidence – to match what happens if a variable or component of a system is changed.
2C: Use and/or develop a model of simple systems with uncertain and less predictable factors.
2D: Develop and/or revise a model to show the relationships among variables, including those that are not observable but predict observable phenomena.
2E: Develop and/or use a model to predict and/or describe phenomena.
2F: Develop a model to describe unobservable mechanisms.
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.
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.
4F: Consider limitations of data analysis (e.g., measurement error), and/or seek to improve precision and accuracy of data with better technological tools and methods (e.g., multiple trials).
Practice 5: Using mathematics and computational thinking
5A: Use digital tools (e.g., computers) to analyze very large data sets for patterns and trends.
5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions.
5C: Create algorithms (a series of ordered steps) to solve a problem.
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
6E: Apply scientific reasoning to show why the data or evidence is adequate for the explanation or conclusion.
6F: Apply scientific ideas or principles to design, construct, and/or test a design of an object, tool, process or system.
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
8C: Gather, read, and synthesize information from multiple appropriate sources and assess the credibility, accuracy, and possible bias of each publication and methods used, and describe how they are supported or not supported by evidence.
NRC Crosscutting Concepts
1. Patterns:
1C: Patterns can be used to identify cause and effect relationships.
2. Cause and Effect:
2B: Cause and effect relationships may be used to predict phenomena in natural or designed systems.
4. Systems and Systems models
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.
Assignment
Review the activities from Lesson 4 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 2" under the heading Lesson 4.
Lesson Objectives
- Develop their own design for a computational science project based on the Greenhouse Gases model [LO1]
- Develop a scientific question that can be answered with data output from running a model [LO2]
- Use the key stages of computational science and model design to develop their question, create their model, and design their experiment [LO3]
- Practice pair programming and iterative design, implement, test cycle [LO4]
- Reflect on human CO2 emissions, as relates to their trips to school, to inform their model design [LO5]
Teaching Summary
Getting Started – 5 minutes
1. Review of the previous day’s lesson and concepts and connection to today’s lesson
Activity #1: Computational Science and Model Design – 20 minutes
2. Introduce key ingredients of computational science model design
3. Define your computational science project
Activity #2: Design and develop your model – 20 minutes
4. Agents and environment
5. Interactions
Wrap-Up – 5 minutes
6. What research is necessary to ground your model in reality?
7. How will you check to see if your model is realistic?
Assessment questions
- Give three examples of how students going to school contribute to climate change by creating greenhouse gases. [LO5]
- State what research question you have chosen to investigate and explain why you chose it. [LO2]
- What procedures in the model have you built? Choose one and describe how it works. [LO3] [LO1]
- What aspects of the real world did you choose to include in your model? What did you leave out? Why? [LO3]
Earth and Human Activity
MS-ESS3-3.Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.
MS-ESS3-4. Construct an argument supported by evidence for how increases in human population and per-capita consumption of natural resources impact Earth’s systems.
MS-ESS3-5. Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past century.
NRC Disciplinary Core Ideas
ESS3.C. Human Impacts on Earth Systems
Human activities have significantly altered the biosphere, sometimes damaging or destroying natural habitats and causing the extinction of other species. But changes to Earth’s environments can have different impacts (negative and positive) for different living things.
Typically as human populations and per-capita consumption of natural resources increase, so do the negative impacts on Earth unless the activities and technologies involved are engineered otherwise.
ESS3.D. Global Climate Change
Human activities, such as the release of greenhouse gases from burning fossil fuels, are major factors in the current rise in Earth’s mean surface temperature (global warming). Reducing the level of climate change and reducing human vulnerability to whatever climate changes do occur depend on the understanding of climate science, engineering capabilities, and other kinds of knowledge, such as understanding of human behavior and on applying that knowledge wisely in decisions and activities.
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.
1D: Ask questions to clarify and/or refine a model, an explanation, or an engineering problem.
1E: Ask questions that require sufficient and appropriate empirical evidence to answer.
Practice 2: Developing and using models
2A: Evaluate limitations of a model for a proposed object or tool.
2B: Develop or modify a model—based on evidence – to match what happens if a variable or component of a system is changed.
2C: Use and/or develop a model of simple systems with uncertain and less predictable factors.
2D: Develop and/or revise a model to show the relationships among variables, including those that are not observable but predict observable phenomena.
2E: Develop and/or use a model to predict and/or describe phenomena.
2F: Develop a model to describe unobservable mechanisms.
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.
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.
4F: Consider limitations of data analysis (e.g., measurement error), and/or seek to improve precision and accuracy of data with better technological tools and methods (e.g., multiple trials).
Practice 5: Using mathematics and computational thinking
5A: Use digital tools (e.g., computers) to analyze very large data sets for patterns and trends.
5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions.
5C: Create algorithms (a series of ordered steps) to solve a problem.
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
6E: Apply scientific reasoning to show why the data or evidence is adequate for the explanation or conclusion.
6F: Apply scientific ideas or principles to design, construct, and/or test a design of an object, tool, process or system.
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
8C: Gather, read, and synthesize information from multiple appropriate sources and assess the credibility, accuracy, and possible bias of each publication and methods used, and describe how they are supported or not supported by evidence.
NRC Crosscutting Concepts
1. Patterns:
1C: Patterns can be used to identify cause and effect relationships.
2. Cause and Effect:
2B: Cause and effect relationships may be used to predict phenomena in natural or designed systems.
4. Systems and Systems models
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.
CSTA K-12 Computer Science Standards
|
CT |
Abstraction |
2-12 |
Use abstraction to decompose a problem into sub problems. |
|
CT |
Abstraction |
3A-9 |
Discuss the value of abstraction to manage problem complexity. |
|
CT |
Abstraction |
3B-10 |
Decompose a problem by defining new functions and classes. |
|
CT |
Algorithms |
3A-3 |
Explain how sequence, selection, iteration and recursion are the building blocks of algorithms. |
|
CT |
Modeling & simulation |
1:6-4 |
Describe how a simulation can be used to solve a problem. |
|
CT |
Modeling & simulation |
2-10 |
Evaluate the kinds of problems that can be solved using modeling and simulation. |
|
CT |
Modeling & simulation |
2-11 |
Analyze the degree to which a computer model accurately represents the real world. |
|
CT |
Modeling & simulation |
3A-8 |
Use modeling and simulation to represent and understand natural phenomena. |
|
CT |
Modeling & simulation |
3A-8 |
Use modeling and simulation to represent and understand phenomena. |
|
CT |
Modeling & simulation |
3B-8 |
Use models and simulation to help formulate, refine, and test scientific hypotheses. |
|
CPP |
Programming |
2-5 |
Implement a problem solution in a programming environment using looping behavior, conditional statements, logic, expressions, variables and functions. |
|
CPP |
Programming |
3A-4 |
Apply analysis, design and implementation techniques to solve problems. |