Teaching Lesson 5

In this lesson, students will finish coding their chosen modifications. Students will then debug their code, checking to make sure it works as they intended, and fixing errors as they find them. In the second activity, students will use their new model as an experimental test bed. They will modify the question they came up with in Lesson 4 if necessary, and they will run experiments to address this question, using repeated trials at each variable setting. Students will critically analyze their results, as well as their model, and relate it back to the bigger picture – Water as a Shared Resource. Students will reflect on what modeling water as a shared resource has taught them about resource management and their own actions as water users. Students should share their findings with the whole class.

Review the activities from Lesson 5 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 5.

Lesson Objectives
    The student will:
  • Revisit complex adaptive systems concepts and learn how they relate to understanding resource management [LO21].
  • Gain a deeper understanding of impacts on ground water resources through experience creating and experimenting with a water pump model [LO22].
  • Use customized model as an experimental test bed to run experiments [LO23]. Learn that multiple runs of the experiment are needed at each variable setting due to inherent
  • randomness in the model [LO24].
  • Use iterative refinement and apply debugging techniques to isolate and fix errors in code [LO25].
Teaching Summary
Getting Started – 5 minutes
     1. Review of previous day’s lesson and concepts and connection to today’s lesson
     2. Revisit complex adaptive systems concepts

Activity #1: Complete & Debug Code – 15 minutes
     3. Use pair programming to complete the model
     4. Test the new model – trace execution and debug the model

Activity #2: Run Experiments – 15 minutes
     5. Review the question first formulated in the model design
     6. Design experiments to run in the new model
     7. Run experiments, using multiple trials

Wrap-Up – 15 minutes
     8. Analyze results and discuss conclusions
     9. Relate the results back to the bigger picture of sharing resources
     10. Share your model and experimental results with the class

Assessment Questions
  • Describe four characteristics of a complex system and how they relate to a resource management situation [LO21].
  • What local or regional issue impacting water resources was included in your model? What are some of the potential impacts of that factor or condition? [LO22].
  • See student Experimental Design Form [LO23, LO24].
  • Give an example of how you were able to find and fix an error you had in your code [LO25].

NGSS Performance Expectations
Earth and Human Activity
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.

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.

NRC Scientific and Engineering Practices
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
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
Cause and Effect
Cause and effect relationships may be used to predict phenomena in natural or designed systems.

Stability and Change
Stability might be disturbed either by sudden events or gradual changes that accumulate over time.

Energy and Matter
The transfer of energy can be tracked as energy flows through a designed or natural system.

Patterns can be used to identify cause and effect relationships.
Graphs, charts, and images can be used to identify patterns in data.

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

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