All materials in this journal subject to copyright by the American Library Association may be used for the noncommercial purpose of scientific or educational advancement granted by Sections 107 and 108 of the Copyright Revision Act of 1976. Address usage requests to the ALA Office of Rights and Permissions. WHY DATA MATTERS

Kristin Fontichiaro [email protected]

Jo Angela Oehrli [email protected]

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Introduction comprehend these resources skills, building out our toolkits with From their first research project, altogether. mini-lessons and high-leverage data students notice numbers. From literacy rules of thumb that bridge how many toes a polar bear has to School librarians can play a the gap between awareness and the population of Azerbaijan to the significant role in helping action. percentage of Americans who own students gain understanding of iPhones, student researchers sense real-world numbers, , How Do You Eat an Elephant? charts, graphs, and visualizations. instinctively that numeric data is One Bite at a Time a powerful way of communicating Librarians are unique cross- information. Even though students disciplinary pollinators who can Few librarians received formal often believe numbers convey an air fill the gaps between subject areas instruction in statistics. Anecdotal of authority in their work, their lack and help students gain skill in evidence points to a profession of critical awareness is undermining comprehending and critically dominated by humanities and their success. evaluating data at home, at school, social science majors with little and in life. We collectively refer to collegiate practice in data and As many states move forward with these skills as and define statistics. On our campus, no adoption or adaptation of the data as: school library candidates since College, Career, and Civic Life 2010 have had STEM (science, (C3) Framework for Social Studies 1. Information represented technology, engineering, math) State Standards, Common Core numerically via raw numbers, backgrounds, and we imagine State Standards, and/or Next percentages, percentiles, the situation is similar elsewhere. Generation Science Standards, averages (mean, median, mode), Few library schools incorporate students are expected to be fluent etc. data or into with data: to collect and analyze it, courses, and create figures and tables, integrate 2. Information that can be used few school library programs require quantitative information, and move algorithmically to determine coursework in research methods, fluidly between text and visually compatibility (OKCupid), where statistical literacy would be a represented numerical information. fitness levels (Fitbit), personality core learning objective. If you are (BuzzFeed quizzes), etc. among the few who are well-versed Despite these formal expectations, in data and statistics, we salute you. students receive little guidance on 3. Numerical information Everyone else, keep —we how to move nimbly between text rendered visually (charts, graphs, see a terrific opportunity ahead for and numbers beyond what examples coded maps, tables, etc.) to librarians. they see in textbooks or instructions aid in pattern-finding and for in-class controlled lab comprehension. (Fontichiaro Tackling self-study in data and experiments. There is a disconnect and Abilock 2015) statistical literacy can be a challenge. between classwork and the data and In our new IMLS-funded project statistical literacy skills needed Michael Bowen and Anthony Bartley to develop data literacy as a subset beyond the classroom. Whether wrote, “Data literacy is important of information literacy skills, we researching cancer statistics or for your students [...] because data have concentrated on how students the best car to buy, students don’t are used to argue and persuade read, comprehend, evaluate, and often have a strong sense of what people to, among other things, vote synthesize data and not on how they those numbers mean. Students for political agendas […] or lease a create and organize data via lab often believe that numbers are car. An improved understanding experiments. In doing so, we have objective, though data in the real of data practices means that better identified six significant themes for world is rarely so. In fact, visualized questions can be asked” (2014, ix) school librarians to consider. data—even from authoritative and better decisions made. sources—can sometimes be anything One: Statistical Literacy To build students’ capacity as but. School librarians increasingly Statistics flood news articles, thoughtful, active citizens in this recognize that students either make Facebook feeds, and scholarly brave new world, we must first build poor decisions about the quality of journals. School librarians and our own data literacy capacity. Given statistics, data, and visualizations, their students must critically “read,” limited time and access to students, or that they lack the ability to contextualize, and interpret raw we must distill and prioritize data

22 Knowledge Quest | What Makes a Literacy? All materials in this journal subject to copyright by the American Library Association may be used for the noncommercial purpose of scientific or educational advancement granted by Sections 107 and 108 of the Copyright Revision Act of 1976. Address usage requests to the ALA Office of Rights and Permissions.

School librarians can play a significant role in helping students gain understanding of real‑world numbers, statistics, charts, graphs, and visualizations. Librarians are unique cross‑disciplinary pollinators who can fill the gaps between subject areas and help students gain skill in comprehending and critically evaluating data at home, at school, and in life.

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and synthesized data. Discerning Three: Data in Argument One student-friendly entry point correlation from causation; Our students can assemble for interacting with is recognizing the difference in the random bits of factual data. citizen science. Students should meaning of mean, median, and However, it takes far more skill to recognize that projects like eBird mode; understanding what margin understand how data is used—both and the emerging PhotosynQ of error signifies in polling data; informationally and persuasively— invite the crowd-sourcing of data and recognizing potential biases to support arguments in resources for the greater good (monitoring in collected data, among other students examine, and then birds’ locations and migration, skills, are critical for reading for students to create viable and tracking photosynthesis scholarly research, understanding arguments themselves. These levels in leaves, respectively) but arguments in popular media, arguments could take the form of must incorporate mechanisms and interpreting government statistics embedded as evidence in to address data authenticity and documents. For example, a research paper, shared charts accuracy. However, students MyFitnessPal released a list of the and graphs with tweaked or non- must also realize that some ten healthiest and least healthy standardized elements, advertising, data-collection projects start out states (MyFitnessPal Staff 2015). A or infographics. with good intentions—such as savvy librarian asks, “How did they the failed inBloom initiative to gather the data?” and discovers Infographics have emerged in passively monitor identifiable that the list was determined based many schools as a novel way for student data and achievement for only on MyFitnessPal users. She students to represent what they decades—but backfire by making then recognizes that those users have learned, yet many school too much personal data vulnerable might not be representative of all librarians with whom we spoke to outside access. Microsoft residents. expressed dissatisfaction with researcher danah boyd has called students’ work, stating that it too for more understanding of data Two: Data Visualization often contained disconnected facts and statistical literacy as tools to navigate these issues (Pearle 2015; Having skills to create and com- and lacked a cohesive argument. Hardy 2012). prehend mapped data, graphs, pie Similarly, a 2013 survey indicated charts, and emerging forms of that citizens in the UK overlooked visualizations will help students statistics that would correct their Five: Personal Data Management effectively navigate visually rich misconceptions on topics like the From Google’s personalized information sets. At a session rates of teen pregnancy and crime search results to Facebook’s at the 2015 Research Relevance (Ipsos MORI 2013). custom ads, students have daily Conference, librarians shared experiences—often unbeknownst to their concerns that the emotional Four: Big Data and Citizen them—captured as their clicks and overtones (e.g., color, icons) Science likes are converted into actionable used in these visualizations have data. While students might like Recent media reports lament powerful influence over students. seeing relevant ads or music society’s “Big Data Problem” One critical question posed in the recommendations that match their (Kopytoff 2014; Pena conversation that our project will favorites, few know it is because Gangadharan 2014; and Salmon address was, “Which comes first? of the breadcrumb trail they leave 2014). More and more data is Learning how to make graphics? Or behind. Students may think the being collected, often without how to interpret them?” Addition- website CNN.com is serving up citizens’ knowledge, via frequent- ally, the need for data visualization the news to them, but they are shopper cards, step counters, skills across library types was usually unaware that as many as social media, and more. Some evident at the 2015 American fourteen bots are following their data is life-saving, such as DIY Library Association Annual Con- actions and converting their clicks systems that help parents monitor ference, where approximately 150 into data, according to a recent their children’s Type 1 diabetes librarians gathered for a two-part experiment conducted with Google by transferring insulin data data visualization session, the Chrome extension Ghostery, which temporarily and anonymously conference’s only session on data monitors webpages for beacons, online (Nightscout Project 2015). literacy. advertising, and click-counting Careful human interpretation of tools. big data is required for positive outcomes to be achieved.

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School librarians have long taught digital citizenship and the importance of being cautious about personal information Librarians must extend their credibility lessons shared online. As students use the to help students recognize that today’s open Web for research, they need to be aware that even if they do online content creators and social not enter their names or phone numbers online, information networks are engaged in a balancing about them—their search habits, act between maximizing advertising their choices of which webpages to visit—may be attaching themselves revenues and delivering quality content. to their Internet cookies or Google account. Librarians must extend This nuanced understanding goes beyond their credibility lessons to help more-obvious credibility markers such as students recognize that today’s online content creators and relevance, authorship, or currency into a social networks are engaged in a balancing act between maximizing recognition that information online is a market- advertising revenues and delivering driven economy—and editorial choices may quality content. This nuanced understanding goes beyond more- be influenced accordingly. obvious credibility markers such as relevance, authorship, or currency into a recognition that information online is a market-driven economy—and editorial choices may be influenced accordingly.

Six: Ethical Data Use Data is not inherently good or bad, but it can be framed, edited, manipulated, or otherwise modified for unethical purposes (such as swaying voters by citing a small or outdated study)—or simply to confuse (Is “take an additional 25% off our half-off prices” the same as 75% off?). Just as students need practice in rhetoric and information literacy, students also need practice learning to create and evaluate data-related arguments and information. This may seem like a minor extension of

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existing practice. We already tell Librarian Action Steps • Abilock, Debbie, and Connie our students to use information For us, data literacy has been an Williams. 2014. “Recipe for an accurately and to cite sources. Is acquired taste, and we suspect the Infographic.” Knowledge Quest 43 using data accurately and citing same may be true for you. Kristin’s (2): 46–55. it any different? We believe that interest grew from Debbie In-depth guidance on constructing there is a difference because of argument-rich infographics. students’ inherent belief that Abilock’s gentle but persistent numbers are infallible. We must prodding that the big data • Best, Joel. 2013. Stat-Spotting: train our brains—and theirs—to movement was something to watch. A Field Guide to Identifying Dubious remember to stop and analyze At the University of Michigan Data, updated and expanded numerical arguments, not Library, Angie currently chairs ed. Berkeley, CA: University of just text-based ones. Ethical the data information literacy task California Press. informational use is more than force, which focuses on exploring Uses friendly, easy-to-read language to merely citing sources. In fact, we existing data literacy teaching provide rules of thumb to keep in mind can leverage discussions about strategies as well as creating new while reading for pleasure or scholarship. ethical use of personal data— teaching practices in this area. something of deep personal She has been working for several • Fontichiaro, Kristin. 2014. value—to ground discussions years on initiatives to develop her Creating and Understanding Infographics. of citation, an abstract concept colleagues’ understanding of data Ann Arbor, MI: Cherry Lake. the value of which can be more literacy. We have found these books A child-friendly overview of infographic difficult for teens to grasp. and resources to be helpful: comprehension and construction.

Kristin Fontichiaro is coprincipal investigator Jo Angela (Angie) Oehrli is coprincipal for the Supporting Librarians in Adding Data Literacy investigator for the Supporting Librarians in Skills to Information Literacy Instruction project, Adding Data Literacy Skills to Information funded by the Institute of Museum and Library Ser- Literacy Instruction project. A former teacher in vices, and a clinical assistant professor at the University an alternative high school and in middle schools, of Michigan School of Information in Ann Arbor. A member of AASL, she Angie works as an instructional librarian and has coauthored a chapter serves on ALA’s Book Links advisory board. She was awarded the 2015 on the University of Michigan (U-M) librarians’ data Joan Durrance Community Engagement Award and the 2014 Excellence in professional development project for The New Information Instruction Award from the University of Michigan School of Information. Literacy Instruction (Rowman and Littlefield 2015). At U-M she Booklist named her Makers as Innovators series one of the 2014 Top collaborates on instructional strategies with diverse groups, including Ten Series Nonfiction (author and series editor). She recently edited the e- the campus Women in Science and Engineering Residence Program. book essay collection Information Literacy, or How I Learned to As the former chair of the University of Michigan Library Instructor Stop Worrying and Love Library Instruction . She coauthored the chapter “Digital Badges: in the area of instruction. She co-created the Michigan Instruction Purposeful Design in Professional Learning Outcomes for K–12 Educators” Exchange, a low-cost, statewide conference for instruction librarians. in Foundations of Digital Badges and Micro-Credentials: She is an adjunct lecturer in the University of Michigan School of Demonstrating and Recognizing Knowledge and Competen- Information, focusing on instructional practices for librarians and cies (Springer in press). She also authored the children’s books Hacking information professionals, and teaches both basic and advanced digital Fashion: Denim, Hacking Fashion: Fleece, Hacking Fash- research methods courses for the U-M College of Literature, Sciences, ion: T-Shirts, Design Thinking, Watch It! Researching with and the Arts. She has also chaired the Top Twenty Committee for ALA’s Videos, and Review It! Helping Peers Create Their Best Work Library Instruction Round Table and currently serves on the LOEX (Cherry Lake Publishing). She blogs at . Advisory Council.

26 Knowledge Quest | What Makes a Literacy? All materials in this journal subject to copyright by the American Library Association may be used for the noncommercial purpose of scientific or educational advancement granted by Sections 107 and 108 of the Copyright Revision Act of 1976. Address usage requests to the ALA Office of Rights and Permissions.

• Hoelter, Lynette. 2014. Conclusion rates, despite the legal ban on “red- “Data, Data Everywhere Data is more than charts, graphs, lining”? It already is (Knott 2015). and Not a Number to and spreadsheets. It is being used in Do schools and libraries have a Teach.” ways to shape how we view the world tions figure this out? Our answer is (accessed December 15, 2015). and our role within it. In a world that we unequivocally do. A welcoming overview of data and where everyone is an author online, statistics by an advisor to our project. data use is at the crux of teens’ daily These are the questions that affect • Wheelan, Charles J. 2013. Naked lives. Never has there been a more all of our students. As part of our Statistics: Stripping the Dread from the critical time to declare data as an IMLS-funded grant, we will be pub- Data. New York: Norton. essential literacy for students. Is it lishing professional development Provides user-friendly context for ethical for someone’s social media materials for school librarians and understanding statistics in the real world. profile to be used as evidence against other educators, as well as conduct- Definitely not a stats class textbook! them? Should someone’s Fitbit data ing virtual conferences in summer 2016 and 2017, focusing first on • Yau, Nathan. 2013. Data Points: be used to contradict her testimony? statistical literacy, data in argument, Visualization That Means Something. It already has been (Hambright 2015). and data visualization, with the Indianapolis: Wiley. Should algorithms predicting future A superb introduction to data crimes be used to sentence someone remaining themes scheduled for visualization analysis and construction. now? They already are (Barry-Jester, development in the final year. We Remarkably user-friendly. Yau also Casselman, and Goldstein 2015). invite you to view our progress and publishes the flowingdata.com blog. Should neighborhood data be used to join our e-mail notification list at determine mortgage eligibility and .

Works Cited: Barry-Jester, Anna Maria, Ben scene-with-knife-vodka-called--/ MyFitnessPal Staff. 2015. “The Healthy Casselman, and Dana Goldstein. article_9295bdbe-167c-11e5-b6eb- Habit Index: The Top (And Bottom) 2015. “Should Prison Sentences 07d1288cc937.html> (accessed 10 Healthiest States.” Hello Healthy Be Based on Crimes That Haven’t December 21, 2015). (July 6). com/features/prison-reform-risk- (accessed December 21, 2015). assessment> (accessed December 15, York Times (June 4). (accessed Bowen, Michael, and Anthony Bartley. (accessed December 21, 2015). December 21, 2015). 2014. The Basics of Data Literacy: Helping Your Students (And You!) Make Ipsos MORI. 2013. “Perceptions Are Pearle, Laura. 2015. “Big Data and Sense of Data. Arlington, VA: NSTA. Not Reality.” 2013. (accessed Dressed-Up Data: Helping Students wrong.aspx> (accessed December 21, December 21, 2015). Unpack Data Visualizations.” 2015). Concurrent session presented at Pena Gangadharan, Seeta. 2014. “The the American Association of School Knott, Jeff. 2015. “How to Use Dangers of High-Tech Profiling, Librarians 17th National Conference, Big Data to Underwrite Safer Using Big Data.” Room for Debate November 6. Columbus, OH. Slides Mortgages.” National Mortgage News. (August 7). (accessed big-data-to-underwrite-safer- the-dangers-of-high-tech-profiling- January 30, 2016). mortgages-1058989-1.html> (accessed using-big-data> (accessed December December 21, 2015). 21, 2015). Hambright, Brett. 2015. “Woman Staged ‘Rape’ Scene with Knife, Kopytoff, Verne. 2014. “Big Data’s Dirty Salmon, Felix. 2014. “Why Quants Don’t Vodka, Called 9-1-1, Police Problem.” Fortune Data (June 30). Know Everything.” Wired (January 7). Say.” Lancaster Online (June 19) (accessed dont-know-everything> (accessed news/local/woman-staged-rape- December 21, 2015). December 21, 2015).

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