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:

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

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.

Collecting, Analyzing, and Evaluating Data

Communicating Data

Assessment

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

Collecting Data

Designing an investigation

Describe in detail the investigation that you have chosen, including the testable question, data to be collected, and the computational tool used to gather, record, and report the data.

Provides general information but insufficient details needed to describe the investigation.

Does not describe the investigation, data to be collected, and the computational tool used to gather, record, and report the data.

Collecting data

Describes in detail the data to be collected in the investigation and how that data will inform the question.

Provides general information but is insufficient in details about the data to be collected.

Does not describe the data to be collected.

Using computational tools

Describes in detail how the computational tool will be used to gather, record, and report the data. Includes frequency of data collection, method of recording data, and how the data was reported.

Provides general information but is insufficient in details about the computational tool used to gather, record, and report the data.

Does not describe the computational tool used to gather, record, and report the data OR the tool described is not computational.

Data

Provides a complete set of data points for the investigation with a minimum of 20 data entries.

Provides an incomplete report of data points needed for the investigation; less than 20 data entries but more than 10.

Provides an incomplete report of data points needed for the investigation; provided

Reflection

Provides detailed reflection on how the computational tool benefitted/hindered the investigation and possible changes needed in the collection of data, and any further data needed to inform the investigation

Provides general reflection on how the computational tool benefitted/hindered the investigation OR possible changes needed in the collection of data, and any further data needed to inform the investigation.

Provides brief/missing reflection on how the computational tool benefitted/hindered the investigation AND possible changes needed in the collection of data, and any further data needed to inform the investigation.

Designing an investigation

Describe in detail the investigation that you have chosen, including the testable question, data to be collected, and the computational tool used to gather, record, and report the data.

Provides general information but insufficient details needed to describe the investigation.

Does not describe the investigation, data to be collected, and the computational tool used to gather, record, and report the data.

Collecting data

Describes in detail the data to be collected in the investigation and how that data will inform the question.

Provides general information but is insufficient in details about the data to be collected.

Does not describe the data to be collected.

Using computational tools

Describes in detail how the computational tool will be used to gather, record, and report the data. Includes frequency of data collection, method of recording data, and how the data was reported.

Provides general information but is insufficient in details about the computational tool used to gather, record, and report the data.

Does not describe the computational tool used to gather, record, and report the data OR the tool described is not computational.

Data

Provides a complete set of data points for the investigation with a minimum of 20 data entries.

Provides an incomplete report of data points needed for the investigation; less than 20 data entries but more than 10.

Provides an incomplete report of data points needed for the investigation; provided

Reflection

Provides detailed reflection on how the computational tool benefitted/hindered the investigation and possible changes needed in the collection of data, and any further data needed to inform the investigation

Provides general reflection on how the computational tool benefitted/hindered the investigation OR possible changes needed in the collection of data, and any further data needed to inform the investigation.

Provides brief/missing reflection on how the computational tool benefitted/hindered the investigation AND possible changes needed in the collection of data, and any further data needed to inform the investigation.

Analyzing Data

Data set and question description

Describes in detail the data set that you have chosen, type of data that is present, and the question you identified to explore.

Provides general information but is insufficient in details needed to describe the data set, type of data that is present, and the question you identified to explore.

Does not describe the data set, type of data that is present, and the question you identified to explore.

Spreadsheet

Provides a spreadsheet with a minimum of 20 data points.The spreadsheet includes appropriate labels and titles to inform the reader of what data is present.

Provides general information but is insufficient in details needed to describe the data set, type of data that is present, and the question you identified to explore.

Provides a spreadsheet with less than 10 data points. The spreadsheet is missing labels/titles to inform the reader of what data is presented.

Data Organization

Data is organized to assist in the analysis of the data set.

Some of the data is organized but does not assist in analysis of the data set.

Data set is not organized.

Explanation

Provides thorough description of the method used to organize the data within the spreadsheet and identifies relationships between variables.

Provides general information, but insufficient details needed to describe the method used to organize the data within the spreadsheet or any relationships that were identified.

Does not describe the method used to organize the data within the spreadsheet or any relationships that were identified.

Reflection

Response includes thoughtful and relevant reflections of data analysis, which may include posing questions about the relationship, identifying additional data to inform analysis, and/or explaining unusual findings from the analysis.

Response includes general but insufficient details of data analysis, which may include posing questions about the relationship, identifying additional data to inform analysis, and/or explaining unusual findings from the analysis.

Response does not reflect upon data analysis or provide relevant details.

Data set and question description

Describes in detail the data set that you have chosen, type of data that is present, and the question you identified to explore.

Provides general information but is insufficient in details needed to describe the data set, type of data that is present, and the question you identified to explore.

Does not describe the data set, type of data that is present, and the question you identified to explore.

Spreadsheet

Provides a spreadsheet with a minimum of 20 data points.The spreadsheet includes appropriate labels and titles to inform the reader of what data is present.

Provides general information but is insufficient in details needed to describe the data set, type of data that is present, and the question you identified to explore.

Provides a spreadsheet with less than 10 data points. The spreadsheet is missing labels/titles to inform the reader of what data is presented.

Data Organization

Data is organized to assist in the analysis of the data set.

Some of the data is organized but does not assist in analysis of the data set.

Data set is not organized.

Explanation

Provides thorough description of the method used to organize the data within the spreadsheet and identifies relationships between variables.

Provides general information, but insufficient details needed to describe the method used to organize the data within the spreadsheet or any relationships that were identified.

Does not describe the method used to organize the data within the spreadsheet or any relationships that were identified.

Reflection

Response includes thoughtful and relevant reflections of data analysis, which may include posing questions about the relationship, identifying additional data to inform analysis, and/or explaining unusual findings from the analysis.

Response includes general but insufficient details of data analysis, which may include posing questions about the relationship, identifying additional data to inform analysis, and/or explaining unusual findings from the analysis.

Response does not reflect upon data analysis or provide relevant details.

Evaluating Data

Data set description

Describes in detail the dataset and the overall meaning.

Does not describe the dataset and/or the overall meaning.

Bias

Provides a thorough description of the source of the data and identifies interests or bias that the source may have.

Provides insufficient description of the source of the data and identified interests or bias that the source may have.

Measurement errors

Provides thorough description of how the data was collected and potential measurement errors.

Provides insufficient description of how the data was collected and potential measurement errors.

Excluded data

Describes in detail data that could have been excluded from this dataset.

Provides insufficient description of data that could have been excluded from this dataset.

Implications

Provides detailed narrative describing how bias, error, or exclusions could have changed the meaning or findings from this dataset.

Provides insufficient details describing how bias, error, or exclusions could have changed the meaning or findings from this dataset.

Data set description

Describes in detail the dataset and the overall meaning.

Does not describe the dataset and/or the overall meaning.

Bias

Provides a thorough description of the source of the data and identifies interests or bias that the source may have.

Provides insufficient description of the source of the data and identified interests or bias that the source may have.

Measurement errors

Provides thorough description of how the data was collected and potential measurement errors.

Provides insufficient description of how the data was collected and potential measurement errors.

Excluded data

Describes in detail data that could have been excluded from this dataset.

Provides insufficient description of data that could have been excluded from this dataset.

Implications

Provides detailed narrative describing how bias, error, or exclusions could have changed the meaning or findings from this dataset.

Provides insufficient details describing how bias, error, or exclusions could have changed the meaning or findings from this dataset.

Communicating Data

Data set description

Describes in detail the data set that you have chosen.

Provides insufficient details to describe the data set.

Audience Demographic

Describes in detail the audience to which you will be presenting the information and why. Description includes at least three key demographic details.

Does not describe the intended audience and why. Description includes less than three key demographic details.

Representation

Provides visual representation for the selected data set. Includes appropriate labels, keys, and design elements for interpretation.

Does not provide a visual representation for the selected data set and/or include insufficient labels, keys, and design elements for interpretation.

Message of Visualization

Describes the message of the visualization and how it relates to the meaning of the overall dataset.

Provides an insufficient description of the message of the visualization and/or how it relates to the meaning of the overall dataset.

Design of Representation for Target Audience

Describes in detail how the chosen design will aid the target audience in interpreting the data set.

Provides an insufficient description of how the chosen design will aid the target audience in interpreting the data set.

Data set description

Describes in detail the data set that you have chosen.

Provides insufficient details to describe the data set.

Audience Demographic

Describes in detail the audience to which you will be presenting the information and why. Description includes at least three key demographic details.

Does not describe the intended audience and why. Description includes less than three key demographic details.

Representation

Provides visual representation for the selected data set. Includes appropriate labels, keys, and design elements for interpretation.

Does not provide a visual representation for the selected data set and/or include insufficient labels, keys, and design elements for interpretation.

Message of Visualization

Describes the message of the visualization and how it relates to the meaning of the overall dataset.

Provides an insufficient description of the message of the visualization and/or how it relates to the meaning of the overall dataset.

Design of Representation for Target Audience

Describes in detail how the chosen design will aid the target audience in interpreting the data set.

Provides an insufficient description of how the chosen design will aid the target audience in interpreting the data set.

Tools

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

Popular Searches

Next Generation Science

How data practices can be used in the classroom

Look Fors

Prompting Questions

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

Resources and Examples

Collecting, Analyzing, and Evaluating Data

Communicating Data

Assessment

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

Tools

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

Popular Searches

Popular Searches

Next Generation Science

Data Practices

How data practices can be used in the classroom

Look Fors

Prompting Questions

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

Resources and Examples

Collecting, Analyzing, and Evaluating Data

Communicating Data

Assessment

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

Tools

Collecting Data

Analyzing Data

Evaluating Data

Communicating Data

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Popular Searches