Teaching Lesson 4
First, help students understand the computational science cycle and where they are in it. Next, help them limit the scope their projects. (The most common mistake students make is to try to do too much.) This leads to a second activity where they start designing and implementing their model.
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 3" under the heading Lesson 4.
Lesson Objectives
The student will:
- Develop an original design for a computational science project
- Develop a scientific question that can be answered with data output from running a model
- Use abstraction to develop an idea for a model
- Use the computational science cycle and Project Design form to develop their question, model, and experimental design
- Practice pair programming and iterative design, implement, test cycle
Getting started – 5 minutes (Review)
1. Review of the previous day’s lesson and concepts and connection to today’s lesson
Activity #1: Computational science cycle – 20 minutes (New Learning)
2. Introduce computational science cycle
3. Define your computational science project
Activity #2: Design and develop your model – 20 minutes (Creative / Discovery)
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 (suggested):
● Ask students to describe their use of scientific practices by filling out the Scientific Practices with Computer Modeling & Simulation form.
NGSS Performance Expectations
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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
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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
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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
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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
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CT |
Abstraction |
2-12 |
Use abstraction to decompose a problem into sub problems. |
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CT |
Algorithms |
3A-3 |
Explain how sequence, selection, iteration and recursion are the building blocks of algorithms. |
|
CT |
Connections to other fields |
2-15 |
Provide examples of interdisciplinary applications of computational thinking. |
|
CT |
Data representation |
3A-12 |
Describe how mathematical and statistical functions, sets, and logic are used in computation. |
|
CT |
Modeling & simulation |
1:6-4 |
Describe how a simulation can be used to solve a problem. |
|
CT |
Modeling & simulation |
2-9 |
Interact with content-specific models and simulations to support learning and research. |
|
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. |
|
CT |
Modeling & simulation |
3B-9 |
Analyze data and identify patterns through modeling and simulation. |
|
CPP |
Data collection & analysis |
2-9 |
Collect and analyze data that are output from multiple runs of a computer program. |
|
CPP |
Data collection & analysis |
3B-7 |
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 4: Create your own ecosystem model
(EDS) Analyzing real-world phenomena in their schools and neighborhoods, and asking authentic questions using project-based learning, are effective teaching strategies to engage economically disadvantaged students.
(URG)(FEM) 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. Having students generate the problems and possible solutions, an important scientific practice, is also very motivating for all students, including girls.
(FEM) Careful planning of partners for the on-computer activity is a strategy that encourages participation for the girls in science.
(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.)
(GAT) The teacher can promote autonomy and the forging of authentic connections to the ecosystems content and to the practice of computer modeling.
(GAT) Grouping students of similar interests and ability, incorporating standards from a higher grade, providing opportunities for self-directed projects are successful strategies for gifted and talented students.