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
- What do you wonder about this topic?
- What question(s) will your dataset address?
- What might someone learn from using the data you collect?
- What will you measure to inform your question?
- What tools can you use to collect this data? What are the affordances and limitations of using a computational tool?
- How frequently will you collect data? Are you observing this data on a certain day or during certain times?
- Is the data you are collecting able to be compiled easily (e.g., using the same units, avoiding open-ended responses to be able to group)?
- Could there be bias in your data (e.g. human error, missing data or questions that lead to certain responses)?
Analyzing Data
- Identify cases and attributes in the raw dataset. How will you modify or use cases/attributes to answer your question?
- How did you manipulate or organize your data? How did this reveal relationships/patterns within the dataset?
- Why do you think the numbers changed? Is there a relationship between numbers/categories?
- Could we use this information to make predictions?
- Can you develop a rule or formula to describe how one variable is related to another variable?
- What new questions do you have about the dataset?
- What other factors (not in the graph) might influence relationships between variables?
Evaluating Data
- If there is more than one data set being used, is the data comparable?
- What data might be missing? How might that data help you better understand the data you have already?
- How was the data collected and by whom?
- What type of organizations can I trust to have collected valid data on this topic?
- How might the interest or bias of groups change the story or findings we can learn from this dataset?
- Could there be bias in the instruments used to collect the data (e.g., questions that lead to certain responses)?
- Is the data self-reported? Think about the implications of self-reported data. How might people’s biases or
- perceptions change the story or findings we can learn from this dataset?
- Think about the implications of observed data. How was the data collected and by whom? How might the process or tool used for data collection change the story or findings we can learn from this dataset?
Communicating Data
- How might you structure your dataset to help you or someone else to answer a question related to your findings?
- How can you provide context or cues (e.g., titles, labels, colors) to help someone else understand your data?
- Why might this data be interesting/important to other people?
- Explain how your design choices help you describe your data to your audience.
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
In
this example, a student explored a question about the novel coronavirus using existing data. Explore the resource to see how the student analyzed and evaluated the dataset 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 collect, analyze, and evaluate data.
Communicating Data
In
this example, a student created an infographic to communicate a data-based claim about the novel coronavirus to a particular audience. Explore the resource to see how the student created a data visualization specific to the users’ needs.
Want to do something like this in your classroom? Use or adapt this template as a resource to design opportunities for your students to communicate data.
Assessment
The following rubrics outline components of each computational thinking data practice that can be utilized to assess student work.
Collecting Data
Collecting Data
| | "Yes" | "Almost" | "Not Yet" |
| 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
less than 10 data entries.
|
| 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
Analyzing Data
| | "Yes" | "Almost" | "Not Yet" |
| 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
Evaluating Data
| | "Yes" | "Not Yet" |
| 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
Communicating Data
| | "Yes" | "Not Yet" |
| 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.
Collecting Data
- Google Science Journal: An app that uses already-available features of devices (e.g., Android, iPhone, iPad) to collect and record data
- Hacking STEM Library: Lesson plans and resources for integrating computational tools into middle school science
- Computing tools with built-in or add-on sensors:
- Tiny programmable computer with sensors to collect and output data. (E.g. light, temperature)
- Standalone sensors or probes:
- A wide variety of sensors and probes designed to collect and output specific data
Analyzing Data
Data sets for analyzing data:
- Our World in Data: Data available for download from a variety of current topics
- Climate Data Sets: Climate data from data.gov
- NASA Vital Signs: Data from NASA tracking CO2, temperature, sea level and arctic ice
- Weather Underground: One of many platforms that logs historic weather data for analysis
- CODAP: Data analysis platform with sample datasets on a variety of topics
- Makeover Monday data: A current, trendy dataset on a variety of topics released every week
- Oceans of Data: A collection of educational resources to integrate data practices into instruction
Platforms for analyzing data:
- CODAP: Data analysis tool allowing students to explore manipulate and visualize data
- TUVA: Data analysis tool allowing students to explore manipulate and visualize data
- Google Sheets: Online spreadsheet with data analysis features
- Numbers: Spreadsheet with data analysis features for Apple products
- Microsoft Excel: Spreadsheet program for purchase with data analysis features
Communicating Data
Platforms for creating data visualization:
- Data GIF maker: Create simple, animated infographics in seconds
- VISME: Create presentations, infographics, or other visuals
- Easely: Simple infographic maker
- Canva: Create a variety a designs and layouts
Platforms for exploring data visualization:
- Visual Capitalist: Data visualizations about many current topics
- Information is Beautiful: Data visualizations about a variety of trendy topics
- Makeover Monday: Hundreds of data visualizations are submitted to a common data set on a weekly basis. Some select visualizations are featured on this blog.
- Dear Data: Beautiful hand-drawn data visualizations detailing daily life sent between pen pals
