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Why data literacy matters


This lesson explores why data journalism matters and exposes students to the importance of data and math literacy, even as journalists. Students will learn how the changing career field of journalism now requires students to understand the role of big data, how to gather it and how to visualize it. Finally, students will assess their own data literacy skills.


  • Students will examine why education and industry professionals believe data literacy is a relevant skill in today’s journalism job market.
  • Students will evaluate those arguments for data literacy in high school.
  • Students will evaluate their own level of math and data literacy and create a plan for improvement.

Common Core State Standards

CCSS.ELA-LITERACY.RI.9-10.8 Delineate and evaluate the argument and specific claims in a text, assessing whether the reasoning is valid and the evidence is relevant and sufficient; identify false statements and fallacious reasoning.
CCSS.ELA-LITERACY.RI.11-12.7 Integrate and evaluate multiple sources of information presented in different media or formats (e.g., visually, quantitatively) as well as in words in order to address a question or solve a problem.
CCSS.ELA-LITERACY.W.9-10.1 Write arguments to support claims in an analysis of substantive topics or texts, using valid reasoning and relevant and sufficient evidence.


60 minutes + homework



Article: Why Data Scientists and Marketing Technologists Are the Hottest Jobs
of 2015
Article: ‘Big Data’ Skills Not Being Taught in K-12
Article: These Are the Biggest Skills That New Graduates Lack
Handout: Data literacy skills audit
Butcher paper (one sheet per group of three)
Markers (two colors per group)
Recommended reading (these sites helped inform the personal skills audit):
Information Literacy Standards
Simple statistical analysis
Statistics tutorial

Lesson step-by-step

1. Building background with a concept map10 minutes

On the board, write the phrase “data literacy.” Ask students what ideas or related concepts come to mind when they see this phrase, and write those words on the board, building a concept map that groups like ideas, words or phrases around the central theme of “data literacy.” Students should offer words like “numbers, statistics, skills, etc.” If they are stumped, ask them to break down the phrase into its two words, “data” and “literacy,” and deal with each in turn. Ask prompting questions such as “What does literacy mean? What does it mean to be literate? What is data? What form does data often come in? What do we do with data?”

After 5 minutes of concept mapping, explain that data literacy is the ability to derive meaningful information from data, which are large sets of numbers or statistics. From those numbers and statistics, we can determine facts or make generalizations about what those numbers mean, why those numbers and findings occurred and to what extent those numbers or statistics or reflective of other situations/contexts.

Explain that data literacy requires a mix of skills, including information analysis skills, math and statistical knowledge, and a bit of technical knowledge for evaluating numbers or looking for relationships among those numbers. We can see how these skills overlap to create data literacy in the following diagram, which you could draw on the board.


2. Group read-around — 15 minutes

Explain that students will form groups of three to read three different online articles (linked in resources list above) that argue for students to develop more data literacy skills. Each person in the group will read a different article. They should read it once to understand the premise and then a second time to take guided notes about the arguments presented. The second time students read, they should use a T-chart to take notes. You can model the T-chart on the board using the diagram below. Students should aim to summarize 3-4 points each author presents, and then they should describe the evidence the author uses to support those claims:

Main claims presented by author Evidence to support these claims

3. Build an argument — 25 minutes

Once individuals have finished their reading and note-taking, they should come back to their groups and create one master document of arguments and evidence for becoming more data literate. Using butcher paper, each group should create a large T-chart that lists all the arguments/claims on one side (in one color) and the evidence on the other side (in a second color). Instruct groups not to duplicate claims, so they should talk about their individual findings first and then decide what list of arguments or claims best represents all three articles without repeating ones found across all three. Students should also talk about these claims and whether they agree or disagree as they are making their master list.

As groups finish, you can hang each piece of butcher paper on the white board or somewhere for the entire class to view. Then, as a class, review each list to see whether the groups identify similar or disparate arguments.

4. Debrief and follow-up activity — 10 minutes (and homework)

Spend the last 10 minutes of class debriefing and explaining their homework: a personal data literacy skills audit. Depending on the nature of your class, you might consider opening the discussion by asking students how they feel about these arguments, whether math makes them anxious and/or what careers they are interested in that would require some of these skills. The point of this discussion is to have students begin to think about data literacy in very personal terms, even if it means they need to own up to some “number anxiety.”

Finally, assign the data literacy skills audit for homework.



Advanced students could use the statistics tutorial linked under Recommended Reading to test their advanced statistical knowledge or complete a self-guided tutorial.

Students who require additional support should be given extra time to read the articles or be placed in groups where the articles might be read aloud, if comprehension is an issue.