The educator guides students through computational approaches to shaping, analyzing, and communicating data.

Data does not exist ready-made in the world. Rather, when students use computational thinking to study a phenomenon, they must decide what data to collect and how to organize it. These decisions constitute the process of “shaping data”—first, students define the questions they want to examine with data; second, they go about collecting the data; third, they structure the data in a way that works with the computational tool (e.g., spreadsheet program, statistical software package, visualization tool) that will help them analyze it.

These decisions about collecting and structuring data must be made and carried out even in cases when students are not collecting original data. In the end, these decisions affect which questions can be addressed through computation, as well as the validity and bias of the answers that are ultimately communicated.

Once data has been selected and structured, it can be analyzed using computational tools. Examples of using computational tools to analyze data include:

- Using statistical software to generate descriptions, predictions, and inferences
- Creating simulations of the phenomenon
- Creating visualizations to find patterns in the data
- Creating higher-level abstractions of the data

The results of data analysis are only meaningful when connected back to questions about the original phenomenon being studied. When students skillfully use data to communicate, they draw on practices from a range of subject areas:

- Framing an argument around research questions
- Addressing a particular audience
- Interpreting data and explaining how they support claims
- Making visualizations that accurately present data-based claims
- Acknowledging limitations and bias in the model and considering how other models might lead to different interpretations

This suggested implementation is intended for the end of a unit in which students have already developed a substantial understanding of a big idea or essential question. (Understandings are often described with *essential questions* or *big ideas*. “Integrating computational thinking into curriculum” addresses the nature of understandings.) By this point in the unit, students will have generated many of their own questions. It can be helpful to keep track of these questions, either in your notes or somewhere public such as a bulletin board or course website.

- Before teaching the lesson, carefully read the submission instructions and consider what evidence you will need to gather for your submission. If necessary, make arrangements to videotape the lesson or ask a colleague to observe and take notes. You may also want to plan to take notes immediately after the lesson to help you remember the details.
- Consider the data sources available to students. Depending on the subject area, these might include original observations they could make of the environment around them, datasets available online (e.g., weather data, demographic data), data that could be gathered from research, or even data collected from a simulation. (“Understanding systems with computational thinking” addresses developing systems thinking by exploring computational models. This would be an opportunity to build on such a lesson.)
- Think about what other skills students have at their disposal. Then choose an appropriate computational tool to use in this lesson. For example, a history teacher might consult with students’ math and science teachers to understand students’ skills with data analysis, then choose to use a spreadsheet program such as Excel.
- Plan a series of lessons in which students shape, analyze, and communicate data. Suggested outline:
- Present students with a list of questions and data sources that could potentially be used to answer them. Ask pairs of students to either choose a question and a data source from the list or to propose their own. Consider enlisting the participation of an authentic audience that is affected by the questions and data under study, with whom the students will communicate their findings.
- Once students have selected their questions and data sources, introduce the computational tool. Use a model question to demonstrate how the tool can be used to work with the data and investigate the question. For example, if the tool were Excel, the analysis might include sorting, filtering, and transforming data, before finding a line-of-best-fit for the relationship between two factors.
- Continue pursuing the model question by working aloud with students, and consider how the data can be shaped to allow analysis using the tool. Ideally, several problems will arise (for example, some samples might be missing some data points), and students will have an opportunity to work out how to address them. Summarize and write out the process used to shape and analyze the data.
- Now that the students have worked through the model question as a group, provide the student pairs time to conduct their own shaping and analysis of the data they have selected for their question. This is an excellent time to give students more autonomy and the opportunity to work through issues on their own. An “office hours” model for class time could be helpful, where students come to the teacher for help when they need it (and some groups may be required to check in!)
- When students are nearly through with their analysis, lead a discussion on how to communicate the results. It could be helpful to ask several groups to share their results and volunteer to be the subject of a group brainstorming session on how best to communicate the results to the audience.
- If students will be using a different computational tool to communicate their results (for example, a data visualization tool), introduce and model how to use it.
- Finally, share your results. Students may communicate their findings in a class presentation or with authentic stakeholders who may be affected by the data and any insights the students have discovered. This can be a thrilling experience!

- Blikstein, P., & Wilensky, U. (2007). Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. In Annual Meeting of the American Educational Research Association (AERA 2007), Chicago, USA.

http://bit.ly/2Adbabg - Lehrer, R., & Schauble, L. (2002). Investigating Real Data in the Classroom: Expanding Children's Understanding of Math and Science. Ways of Knowing in Science and Mathematics Series. Teachers College Press, PO Box 20, Williston, VT 05495.
- Ben-Zvi, D., & Arcavi, A. (2001). Junior high school students' construction of global views of data and data representations. Educational Studies in Mathematics, 45(1), 35-65.

http://bit.ly/2fRhT2A

- Shaughnessy, J. M. (2007). Research on Statistics Learning and Reasoning. Second Handbook of Research on Mathematics Teaching and Learning, 2, 957-1010.
- Vacher, H. L., & Lardner, E. (2010). Spreadsheets Across the Curriculum, 1: The idea and the resource. Numeracy, 3(2), 6.

https://serc.carleton.edu/sp/ssac_home/index.html

- diSessa, A. A. (2004). Metarepresentation: Native Competence and Targets for Instruction. Cognition and Instruction, 22(3), 293-331.

http://dx.doi.org/10.1207/s1532690xci2203_2 - Kahn, J., & Hall, R. (2016). Getting personal with big data: Stories with multivariable models about global health and wealth. In Annual Meeting of the American Educational Research Association, Washington.
- Johnson, D. W., & Johnson, R. T. (1979). Conflict in the Classroom: Controversy and Learning. Review of Educational Research, 49(1), 51-69.

http://bit.ly/2h1W7JD - Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88(3), 345-372.

http://bit.ly/2w6KfjC

- Access resources for this micro-credential on Digital Promise’s website dedicated to resources for teaching computational thinking:

https://sites.google.com/digitalpromise.org/computationalthinking

*To earn the micro-credential, you must earn a “passing” evaluation for Parts 1 and 3, and a “Yes” for each component of Part 2. *

*In the assessment of this micro-credential, an educator will guide students in an inquiry project pursuing an authentic research question, through the steps of shaping data, analyzing it with computational tools, and communicating their findings to an audience invested in the results of students’ inquiry. The educator will analyze one student’s learning, and reflect on the successes and limitations of the lesson(s). The three parts of the assessment should fit together as evidence of professional reflective practice. *

(400-word limit)

Please answer the following questions:

- How did students select their research questions? How did you support students in selecting research questions which would be feasible to pursue?
- Describe the data sources and computational tools students used to investigate their questions. What scaffolding did you provide to students in shaping and analyzing their data?
- Describe the audience(s) with whom students shared their findings.

To earn this micro-credential, please submit the following:

**1) Student artifacts**

Submit the following artifacts from **two students**:

- One or more artifacts of the student’s work while the lesson was in progress (e.g., student journal entries, student reflections, data analysis, presentation drafts, the teacher’s or another colleague’s observation notes, an interview with the student) and
- The student’s final product (e.g., a poster, a paper, a recording of a presentation).

**2) Analysis of artifacts**

(800-word limit total)

As you answer the following questions, **select artifacts from one student and refer to specific evidence from the submitted artifacts**.

*Note: If students worked in groups, you may choose to analyze one student’s learning within the group or the learning of the group as a whole. *

**Shaping data**- Describe the process by which the student shaped the data. How did these choices reflect an understanding of the student’s research questions and of the requirements of the computational tool? How did the student’s understanding of the structure of the data develop over the course of the research project?

**Analyzing data**- Describe how the student used the computational tool to analyze the data. What insights, predictions, descriptions, or abstractions of the data did this process yield? How did this use of the computational tool advance students’ skills or understandings?

**Communicating with data**- Describe how the student built an argument which addressed the research questions and the audience. How did the student connect the data to claims? How was the data represented? How did the student address the limitations of the model, its bias, and other possible interpretations?

(300-word limit)

Reflecting on the lesson, what might you change that would support one or more students (not necessarily the student whose work was considered in Part 2) to more effectively use computational thinking in the inquiry process?

Except where otherwise noted, this work is licensed under:

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

http://creativecommons.org/licenses/by-nc-nd/4.0/

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

http://creativecommons.org/licenses/by-nc-nd/4.0/

Download to access the requirements and scoring guide for this micro-credential.

Requirements for Working with Data

How to prepare for and earn this micro-credential - in a downloadable PDF document

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