Computational models are representations of systems with interrelated parts. They represent measurable (quantitative) relationships within systems and change behavior based on different inputs into variables. Computational models are particularly useful for understanding and exploring systems that can’t be observed because of size, time, or visibility.
We have identified the following three sub-practices of understanding systems with computational models:
- Using and Modifying Computational Models to Explore a Driving Question: Students use a computational model to explore a driving question by testing different inputs.
- Creating Computational Models: Students create a computational model depicting relationships between key components of a system.
- Assessing Computational Models: Students compare a computational model to a real-world system to analyze the overall accuracy of the model.
Download the Resources
Explore the resources below to consider how to integrate computational models in your classroom.
How computational models can be used in the classroom
Our teacher partner from Vancouver Public Schools helped students to use, modify, and create computational models to explore scientific phenomena in her middle school science class.
“I always have students create 1-3 ‘I wonder’ questions related to the topic we are discussing. Then they explore a computational model to test out their questions. That was the number one component that engaged kids. Several students from my class chose to create a model on alternative energy sources. One student created a windmill. He changed the number of blades, wind direction and speed of the wind, which all affected how it worked.” – Teacher, Vancouver Public Schools
Look Fors
Teachers will know students are understanding systems with computational models because they may observe the following student actions:
Using and Modifying Computational Models to Explore a Driving Question
- Identifying a question to explore using a computational model
- Setting up multiple and different scenarios to collect data from a computational model
- Making predictions about how the model will behave with different inputs
Creating Computational Models
- Identifying different parts of the system that the model is representing
- Defining relationships between different parts of a system
- Automating relationships between parts of the system with a flowchart or programming/modeling software
Assessing Computational Models
- Considering how the model represents the real-world system
- Considering bias in the outputs of a computational model
Prompting Questions
Ask students to engage in understanding systems with computational models and/or reflect on their process or progress with these prompting questions:
Using and Modifying Computational Models to Explore a Driving Question
- What real-world phenomenon is this model based on?
- How can this model help you better understand this phenomenon?
- What variables can you change?
- What question will you explore with this computational model?
- What scenarios will inform your question?
- What changes would you make to the model to better inform your question?
Creating Computational Models
- What system will you model?
- What are the parts of this system?
- What is the purpose of your model?
- Can you remove any parts of the system and still achieve the purpose of the model?
- Sketch or describe the relationship between the parts of the system.
- Define the relationships between the parts of the system that you will include in your model.
Assessing Computational Models
- If the student created the model: Does each step in your model have the result you intend it to?
- If the student created the model: Does a partner testing your model get the same results as you?
- Are there certain inputs where you do not get the intended result?
- How is this model similar to the system in the real world?
- How is this model different from the system in the real world?
- Would you make any changes to this model to make it more similar to the real world? If so, what would you do?
- Did the creator make any assumptions about the system when he/she created the model? If so, how are those assumptions affecting the model?
Resources and Examples
Explore examples of middle school science activities integrated with computational thinking practices. Although the examples are topic-specific, templates are available for you to design opportunities in different topics or contexts.
Using, Assessing, and Modifying Computational Models
In this example, a student used a computational model to explore a question about disease transmission and social distancing practices. Explore the resource to see how the student used a computational model to answer a driving question.
Want to do something like this in your classroom? Use or adapt this template as a resource to design opportunities for your students to use and modify computational models.
Creating and Assessing Computational Models
In this example, a student created a model of factors that may affect the buoyancy of a submarine. Explore the resource to see how the student created this computational model.
Want to do something like this in your classroom? Use or adapt this template as a resource to design opportunities for your students to create computational models.
Assessment
The following rubrics outline components of understanding systems with computational models that can be utilized to assess student work.
Using a Computational Model to Explore a Driving Question
Using a Computational Model to Explore a Driving Question
| | "Yes" | "Almost" | "Not Yet" |
| System Description | Fully describes the system being simulated in the chosen model and identifies the parts of the system included within the model. | Provides general information but insufficient details about the system being simulated in the chosen model and identifies the parts of the system included within the model. | Does not describe the system being simulated in the chosen model nor identifies the parts of the system included within the model. |
| Driving Question | Identifies a clear and measurable driving question based on the chosen computational model. | Identifies a driving question, but it is not specific or cannot be measured by the chosen computational model. | Does not identify a driving question. |
| Data Collection | Collects multiple data points through the model that answer the identified driving question. | Collects multiple data points, but those data points do not answer the driving question. | Does not collect data through the model. |
| Data analysis | Analyzes data to draw meaningful insight to the driving question. | Analyzes data, but analysis is unrelated to the driving question. | Does not synthesize data. |
Creating Computational Models
Creating Computational Models
| | "Yes" | "Almost" | "Not Yet" |
| Identify a quantifiable system | Describes in detail the systems they are modeling, such as what led them to this system and why it is fitting for creating a model. | Provides general information but insufficient details about what led to this system and why it is fitting for creating a model. | Does not describe the system, what led to this choice, and why it is relevant. |
| Identify component parts of a system | Describes in detail the component parts of the system chosen and breaks down effectively the parts of the system. | Provides general description of component parts of the system, but not in enough detail, or does not effectively describe parts of the system. | Does not describe the component parts of the system. |
| Identify relationships in a system | Identifies in detail the relationships in a system, including relevant mathematical relationships. | Provides general identification of the relationships in a system.
Partially identifies the mathematical relationships in a system, if present.
| Does not identify the relationship in the system. |
| Computational Model | Provides complete evidence of the computational model. At least 10 screenshots or a video of the model working and includes model creation process. | Provides insufficient evidence, less than 10 screenshots or a video of the model working, and does not include the model creation process. | Provides incomplete evidence. There are no screenshots, videos, or photos of evidence of a working computational model. |
| Explanation | Describes in detail how the model was created, what led them to this tool for model creation, and how the model works. | Provides general information but insufficient detail about how the model was created, what led to this tool for model creation, or how the model works. | Does not describe how the model was created, what led to this tool for model creation, or how the model works. |
Assessing Computational Models
Assessing Computational Models
| | "Yes" | "Almost" | "Not Yet" |
| Describes the parts of the system and the relationships clearly | Describes in detail the parts of the system and the relationships demonstrated in the model. | Provides general information but insufficient details about the parts of the system and the relationships demonstrated in the model. | Does not describe the parts of the system used and the relationships demonstrated in the model. |
| Model Representation of Real-Life System and Similarities and Differences Between Model and Real-Life System | Describes in detail how this model represents the real-life system. Includes both similarities and differences between the model and real-world system. | Provides general information but insufficient details about how this model represents the real-life system. Includes either similarities or differences between the model and real-world system | Does not describe how this model represents the real-life system. Does not include similarities and differences between the model and real-world system. |
Tools
Explore this curated list of supports to implement, explore, and promote computational thinking practices in your classroom.
Creating Computational Models
- Scratch: A block-based coding environment for children ages 8-16 with extensive resources for educators
- Sage Modeler: Drag and drop tools for students to illustrate relationships between variables as they design, build, and revise models
- Insight Maker: Students illustrate relationships between variables using diagrams, and then transform the diagrams into simulations
- Loopy: A tool to illustrate relationships between parts of a system
- NetTango: Block-based programming environment in which users can define relationships within systems to create models
- Code Blocks: Create a 3D animation using block-based coding
- Minecraft: Students code objects within the Minecraft virtual environment
Using and Modifying Computational Models
Students can use and modify models they created using any of the “Creating Computational Models” tools above. The following three platforms afford the opportunity to create your own model, but may be challenging for middle school students to navigate individually. On these platforms, we suggest teachers creating computational models or selecting a pre-made simulation for students to use or modify:
- StarLogo Nova: Block-based programming environment for the creation of computational models and simulations rendered in 3D
- NetLogo: Text-based programming environment in which users can define relationships within systems to create models
- C2STEM: Block-based programming environment for students to use and remix computational models to illustrate scientific phenomena
The following two platforms (and many others) include pre-made computational models that illustrate relationships between parts of scientific systems. Remember! Not all interactive simulations are computational models. Be sure you select a computational model because it (1) represents measurable (quantitative) relationships within systems, and (2) changes behavior based on different inputs into variables.
- PhET: Science simulations for Physics, Chemistry, Earth Science, Biology and Earth Science
- Learningscience.org: A curated list of science simulations organized by grade level and NGSS standard
