November 22, 2017

The Advocate's Toolbox

Why Kids Need Data Literacy, and How You Can Teach It

Samantha Viotty’s activity for visualizing data networks has gummy bears
representing people and toothpicks signifying their relationships.
Photo courtesy of Samatha Viotty

Data is all around us, from the output of your Fitbit to interactive maps that track voters to the latest visualization of the New York Times front page. With the rise of mobile devices and wearable technology, data is more available to general audiences, and the amount being generated has also exploded. According to IBM, 90 percent of the world’s data has been created in the last two years.

This vast pool of information is being used to advocate for change, justify decisions, and suggest personal action plans—such as the U.S. Department of Education’s College Scorecard, which offers students data to help them choose a college. It is vital that our students understand and interpret the data, infographics, and visualizations they encounter.

One reason data literacy is vital is that “[i]n what some are calling a ‘post-truth world,’ students seem to focus on numbers a lot,” says Jo Angela Oehrli, learning librarian/children’s literature librarian at the University of Michigan Libraries. Students believe that if a number is connected to information, “it has to be a fact. But numbers are manipulated all of the time….We want students to have a tool kit of questions that they can use to question the data that is out there.”

To this end, Oehrli and Kristin Fontichiaro, clinical associate professor at the University of Michigan School of Information, have been leading an IMLS-funded project called Supporting Librarians in Adding Data Literacy Skills to Information Literacy Instruction. Through data literacy programs and data science training, librarians can ensure that students develop the skills to question and interpret data in the news or that they generate through their day-to-day activities. They might even set some students on the path toward a career in the expanding field of data science.

What are data literacy and data science?

Data literacy refers to the ability to understand, generate, and use data. This can mean everything from being able to sort through the results of a survey to being able to understand the meaning of a complicated graph or chart. It also includes the ability to critically evaluate data and visualizations.

Data science goes beyond data literacy. The term “data science” has been around for decades, but its current usage was advanced by Chuien-Fu Jeff Wu at the University of Michigan in the late 1990s, where he was a professor of statistics. It has exploded in popularity recently, with the rapid increase of accessible sources of data and the tools to work with it, such as Google Forms, which makes it easy to collect data, and Google Charts, which allows users to visualize results. In the face of this burgeoning interest, many colleges and universities are adding programs in response to the growing demand for trained data scientists.

At its heart, data science refers to the analysis of data, usually through automated means, and the interpretation and application of the results. Frequently, it is closely related to computer programming and statistical topics. In fact, much of the current rise in popularity of data science has to do with greater interest in computer programming skills and new hardware and software that makes this work approachable for people of all ages.

As more folks master coding languages such as Python, they also learn how to use coding skills to manipulate, analyze, and interpret the reams of data we produce every day, with popular training sites like Codecademy (codecademy.com) and Lynda.com offering tutorials related to data analysis.


Activities for kids and teens

Many libraries are stepping up to offer programming for students and patrons. At the Carnegie Library of Pittsburgh, librarians are always on the lookout for ways to integrate data literacy topics into their services for children and teens. The results range from incorporating local data into activities featured in their Super Science Kits, which provide a collection of materials and lesson plans on a particular STEM topic for use both in the library and at outside programs that they organize, to partnering with the University of Pittsburgh on an IMLS grant about youth data literacy.

Their work extends beyond the library, too. “We’ve been bringing data literacy activities on the road when we table at different community events,” says Eleanor Tutt, open data and knowledge manager at the Carnegie Library. “A recent activity that was popular was a data keychain—we selected 10 yes/no questions relevant to the event, and participants chose a color of pony bead to represent ‘yes’ and a color for ‘no’ and then created a coded keychain representing personal data about themselves.” In the end, each participant has a unique keychain with a pattern that represents their responses to the list of questions. These simple activities get participants thinking in new ways and realizing that “they and their families are creating data, perhaps unknowingly, through their actions,” Tutt says.

Samantha Viotty, a graduate student at the Emerson College Engagement Lab, has partnered with the Boston Public Libraries on her DIY Data Art program, which aims to teach data literacy through self-guided worksheets incorporating hands-on visualization activities.

In one, called Emoji Data Stories, teens are asked to visually represent answers to a question, such as their favorite activity or food, using only emojis in order to prompt them to consider “the concept of isotypes and how to display information in a visual way,” Viotty says. This process makes them consider how information can be conveyed without words—and it’s fun for them to play around with emojis. The Gummy Bear Networks activity has teens build network visualizations that show how a group of people are connected to one another, with gummy bears representing the people and toothpicks signifying the relationships between them.

In the Cell Phone Data Paper Pie Charts activity, teens create a pie chart representing the data usage of each app on their cell phones. This exercise “includes a conversation about which applications use their data and how the data is being collected and used,” which can add an interesting dimension to the exercise, Viotty explains.

This topic can also be combined with computer programming instruction to create events with wider appeal. Robyn Allen, an MIT graduate and licensed technology teacher, has partnered with four Boston-area public libraries to offer hackathon sessions on data science for teens. These half-day events introduce participants to the Python programming language and teach them how to use it for a data analysis project. Since so much data is publicly available, the events can offer opportunities to work with everything from sports data to data from educational apps. As Catherine Halpin, youth technology coordinator at the Boston Public Library, where one such program was recently offered, notes, the goal is that participants “walk away feeling this subject material is demystified, that they know they are capable, and confident to continue this path of learning.”

Teaching tools and resources

There are many resources that support teaching data literacy, no matter your background. Tools such as Mentimeter, Socrative, and Poll Everywhere allow you to collect responses from students on the spot and generate visualizations that represent the information graphically.

Easy-to-use infographic tools such as Infogram and Piktochart can be used for projects about data advocacy and storytelling. These tools make creating a compelling infographic straightforward through a combination of intuitive features and online tutorials.

When you’re ready to venture into data analysis projects, Databasic.io’s suite of tools offers web-based ways of exploring and understanding data, complete with activity guides designed for use with teens. Best of all, there is an increasing amount of open data available from local groups and government agencies that can offer relatable and interesting datasets for teens to analyze.

All of these tools can serve as the basis of a larger conversation about the role of data in public discussions, such as the way that schools use student data to make curriculum decisions or how local governments track traffic data to make decisions about signage and stoplights, and what questions students should ask when they encounter data and visualizations in their daily lives.

For those who want to go even further, the University of Michigan initiative Supporting Librarians in Adding Data Literacy Skills to Information Literacy Instruction has brought together a group of data and curriculum design experts to create professional development resources for librarians. Under the guidance of Fontichiaro and Oehrli, this team hosted a free virtual conference in the summer of 2016, and they are preparing for a second one scheduled for July 20–21 2017.

The team has also written two books due out this fall, Creating Data Literate Students, which collects chapters by the curriculum experts on teaching data literacy in the classroom, and Data Literacy in the Real World: Conversations and Case Studies (both Maize Books), which will collect approximately 40 case studies about data literacy. The duo is also presenting a poster about their work, “Real Strategies to Address Fake News: Librarians, Data Literacy, and the Post-Truth World,” at the 2017 ALA Annual Conference this month.

The overarching message from all and other data initiatives? Don’t be scared by it. The goal is that “high school librarians will start to feel comfortable talking about data literacy issues with their students and fellow teachers,” Oehrli says.

“So many librarians were humanities majors with little exposure to data, and so many classroom teachers think of data in terms of test scores,” adds Fontichiaro. “By focusing on high-impact strategies, we want librarians and teachers to feel empowered by data, not victimized by it. Our early efforts show that a little knowledge has significant impact.”

Seven Places To Find Public Data

Data.gov The U.S. federal government’s portal for open data provides access to a lot of datasets and offers the option to browse by topics, such as health, public safety, and agriculture. The site also links to data collected by cities, counties, and states.

UNData For projects with an international perspective, the United Nations’ data portal not only provides access to international datasets on topics from agriculture to tourism, but also has links to the data portals for many countries around the world.

NYC Open Data Though far from the only city with an open data portal, New York City has one of the nicer, more user-friendly sites. Through this portal, you can find city-wide data about education, recreation, local government, and more.

U.S. Department of Education This site collects data related to the Department of Education, which means that it offers a number of datasets that might interest students, librarians, and teachers. It also offers a link to the National Center for Education Statistics’ Create-A-Graph tool (nces.ed.gov/nceskids/createagraph), which is aimed at younger students.

Centers for Disease Control and Prevention (CDC) Public health data can offer many interesting possibilities in working with data, and the CDC’s website provides access to a variety of health topics and tools and resources related to the datasets.

Global Health Observatory Data Repository For health data at a global level, you can also look to the World Health Organization’s data site, called the Global Health Observatory. Here you’ll find datasets of health problems around the world and related topics, such as air quality and road safety.

Open NASA Space exploration is always an exciting and inspiring topic, so why not incorporate NASA’s work into your data and visualization activities? With the open datasets available on NASA’s data portal, your data project can visualize meteorite landings or analyze NASA’s patents.

Carli Spina is head librarian of assessment and outreach at Boston College Libraries.

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Comments

  1. Thanks for the insight. From Nairobi. I find Open NASA and UNData to be the most relevant for my references.

  2. Christina Keasler says:

    Wonderful article. Thank you so much!

  3. Brady Rochford says:

    Wonderful article. I’m a middle school teacher who is working to ensure students are critical readers, aware of bias and data manipulation in the news and would like to try the Emoji and Gummy Bear activities next year. I’m having trouble finding them on the Emerson Engagement Lab site…it’s great though! Any direct links to those worksheets would be super helpful. Thanks.

    • Chris Gifford says:

      I also would like to try these and would like the resource info.

    • Hi Brady and Chris,

      The DIY Data Art activity worksheets are being revised into a booklet for those who want to use them. They are apart of a longer thesis and have been through an iterative process so that you can get the most out of these activities- still working out some of the challenges that were encountered during the first and second iteration. The worksheets will be available within the next 2 weeks and you can find them, more information about DIY Data Art as well as other resources at itsliteracy.org.

  4. Great article. Thanks!

    If we are going to talk about data literacy, let’s recognize that the word “data” is plural. The singular of the word is “datum.” Curriculum, curricula; criterion, criteria. So if we want students to obtain data literacy, let’s please use the plural, “are,” instead of the singular, “is,” with “data.”

    • Martha Kelly Patton says:

      Not intending to use this thread to belabor a grammatical point, but whether or not “data” is treated as a collective noun that takes a singular verb or a plural noun taking a plural verb is a topic that has been hashed out extensively in a variety of stylebooks and contexts. In general use, data is most often treated as a collective noun that takes a singular verb, with the plural verb form being more limited to scientific and statistical contexts where writers refer to individual information points. I tell students that usage of the word “data” and the conversation about its form is evidence that our language is a living one and changes with use, and they should be aware of their purpose and audience when deciding which form is appropriate. In academic science writing, I am stickler for having them consider the two noun forms; in writing for a more general audience, I ask them to consider what their readers will understand. The Washington Post, New York Times, and The Guardian all call for a singular verb in their style guides. Here’s a link to an article I share with students: https://www.theguardian.com/news/datablog/2010/jul/16/data-plural-singular

  5. Jaimin Patel says:

    Super Article. Other resource I would add is Tuva (tuvalabs.com) which not only provides the curated open datasets for students, but also provides meaningful activities along with it which can be used in classroom settings.

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