Lesson 5: Big, Open, and Crowdsourced Data


Students will complete a jigsaw of three different topics at the intersection of data, computing, and global impacts. These are topics, big data, crowdsourcing, and open data. Students will watch videos or listen to audio recordings about the different topics. Groups will each complete an activity guide about their topic before having individuals from each group share out their findings. The lesson concludes with a review of key points.


This lesson zooms back out from the data analysis process to the ways that is applied in a wide variety of contexts. Students learn how big data, open data, and crowdsourcing apply this process in interesting ways that cleverly modify this process. For a summary of key points of this lesson review the key takeaways in the slides. In short however:

  • Big data: "Collect huge amounts of data so we can learn even more from it"
  • Open data: "sharing data with others so they can can analyze it"
  • Crowdsourcing: "collecting data from others so you can analyze it"

This lesson further builds towards the following lesson on machine learning which explores a different application of the data analysis process.


Lesson Modifications

Warm Up (5 mins)

Activity (30 mins)

Wrap Up (5 mins)

View on Code Studio


Students will be able to:

  • Define and explain the impacts of crowdsourcing, crowdfunding, and citizen science
  • Explain why in some contexts large amounts of data need to be analyzed in parallel and scalable systems
  • Explain the impact of open data on scientific research and discovery


  • Ensure students will be able to access all of the videos / articles linked in the lesson.
  • Review at least the key takeaways and ideally some of the content from each topic to ensure you understand how these topics relate to what students have studied in previous lessons.


Heads Up! Please make a copy of any documents you plan to share with students.

For the Teachers

For the Students

Teaching Guide

Lesson Modifications

Attention, teachers! If you are teaching virtually or in a socially-distanced classroom, please read the full lesson plan below, then click here to access the modifications.

Warm Up (5 mins)

Discussion Goal

Goal: This is designed entirely to be a review of concepts students have previously covered and doesn't foreshadow the lesson of the day. If you feel confident your students are comfortable with this process already then quickly move on to the main activity of the day.

Prompt: With a partner review the data analysis process and for each step talk through:

  • What is this step and why is it important?
  • Where have we done this step together?
  • What could go wrong if you do this step poorly.

Discuss: Have students brainstorm silently on their own, then have them share with neighbors, and finally have them share out with the room.


Today we're going to be looking at a lot of ways that data is being used in exciting and innovative ways. We're going to stop looking just at the data in App Lab and start thinking about the impacts data has on our lives. Along the way we'll talk about how the data analysis process looks different or has been manipulated in different contexts in order to answer questions or make decisions that matter.

Activity (30 mins)

Group: Place students in pairs

Teaching Tip

Complete the Activity Digitally: Students will have a much easier time accessing articles and videos if they complete the activity digitally. Alternately students can complete printed versions of the activity guide but still access links through the digital versions.

Supporting the Jigsaw: In this lesson students do a jigsaw of a number of different topics. Students will need access to computers and should spend roughly 10 minutes in each group listening to audio / video content. During this period circulate the room encouraging them to focus on the questions they've been asked to respond to. This will also help you anticipate or even specifically ask different students to participate during the discussion.

Distribute: Give each pair a copy of the Big, Open, and Crowdsourced Data - Activity Guide

Prompt: With a partner Choose one of the topics Watch the related videos / listen to the podcasts * Take notes and be ready to share responses to the questions on your activity guide

Discuss: Have members from each topic share the conclusions from their watching and research. Make sure that students from each group have time to share

  • What the topic is
  • The key vocabulary they were responsible for researching
  • How this concept uses or modifies the data analysis process
  • Examples of the problems this technique is being used to solve

Wrap Up (5 mins)

Review: Review key takeaways on the slides


Assess: You can collect and evaluate students' activity guides

Standards Alignment

View full course alignment

CSTA K-12 Computer Science Standards (2017)

DA - Data & Analysis
  • 3A-DA-10 - Evaluate the tradeoffs in how data elements are organized and where data is stored.


DAT-2 - Programs can be used to process data
DAT-2.C - Identify the challenges associated with processing data.
  • DAT-2.C.6 - The size of a data set affects the amount of information that can be extracted from it.
  • DAT-2.C.7 - Large data sets are difficult to process using a single computer and may require parallel systems.
  • DAT-2.C.8 - Scalability of systems is an important consideration when working with data sets, as the computational capacity of a system affects how data sets can be processed and stored.
IOC-1 - While computing innovations are typically designed to achieve a specific purpose, they may have unintended consequences
IOC-1.E - Explain how people participate in problem-solving processes at scale.
  • IOC-1.E.1 - Widespread access to information and public data facilitates the identification of problems, development of solutions, and dissemination of results.
  • IOC-1.E.2 - Science has been affected by using distributed and “citizen science” to solve scientific problems.
  • IOC-1.E.3 - Citizen science is scientific research conducted in whole or part by distributed individuals, many of whom may not be scientists, who contribute relevant data to research using their own computing devices.
  • IOC-1.E.4 - Crowdsourcing is the practice of obtaining input or information from a large number of people via the Internet.
  • IOC-1.E.5 - Human capabilities can be enhanced by collaboration via computing.
  • IOC-1.E.6 - Crowdsourcing offers new models for collaboration, such as connecting businesses or social causes with funding.