Data Practices - Digital Promise

Data Practices

Computational thinking allows users to engage in data practices that reflect authentic practices of scientists and engineers.

For example, computational tools can be a powerful way to collect data because they provide an opportunity to collect data more often, more efficiently, and/or more accurately than a human can. Computational data analysis allows us to manipulate large, rich data sets to identify relationships. Computational tools are also an effective way to communicate data about an argument or idea to a wide range of audiences. These data practices are essential components of learning activities for students as they make sense of scientific phenomenon.

We have identified the following four sub-practices of working with data:

  • Collecting data with computational tool(s) and outputting data to prepare for analysis to inform a driving question
  • Analyzing data to identify relationships and make predictions
  • Evaluating data to identify bias in data collection and reporting
  • Communicating data to a particular audience

Download the Resources

Explore the resources below to consider how to integrate computational thinking data practices in your classroom.

How data practices can be used in the classroom

Learn how our partner Jessica Bibbs-Fox, a teacher in Compton Unified School District, transformed the COVID-19 pandemic into an authentic, project-based learning experience that integrated computational thinking data practices into her virtual middle school science class. Read more.

“I built this project around the current phenomena COVID-19 and I wanted my students to be able to actually look at what was being produced on our different news outlets and really look at whether it was valid or not.” – Jessica Bibbs-Fox

Our partner Susan Meija, a teacher in Broward County Public Schools, designed an authentic, project-based learning experience exploring the effects of micro-plastics on aquatic ecosystems while integrating computational thinking data practices into her middle school science class. By contaminating saltwater aquariums with plastic, Susan and her students studied the impact of plastic waste by collecting data on the amount of light blocked by different types of plastic and the changes in the pH of each saltwater environment.

“We collected data and measured it on a daily basis. By putting the data in CODAP they can see how it can be manipulated by the variables. You can compare all of the graphs together in one or individually and they were able to make better comparisons and analyze it.” – Susan Mejia

Look Fors

Teachers will know students are engaging in computational thinking data practices because they may observe the following student actions:

Collecting Data

  • Designing an experiment utilizing computational tools to collect data
  • Collecting data that can be quantified

Analyzing Data

  • Manipulating data with data moves
  • Describing relationships between variables
  • Using data to make predictions

Evaluating Data

  • Identifying bias in data collection and reporting
  • Considering if/how data sources are comparable

Communicating Data

  • Designing a visual representation of data
  • Selecting design features to communicate to a particular audience

Prompting Questions

Ask students to engage in computational thinking data practices and/or reflect on their process or progress with these prompting questions:

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.


The following rubrics outline components of each computational thinking data practice that can be utilized to assess student work.


Explore this curated list of supports to implement, explore, and promote computational thinking practices in your classroom.

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