Understanding Systems with Computational Models

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

Creating Computational Models

Assessing Computational Models

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

Creating and Assessing 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

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.

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

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.

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

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.