Building Literacy for the Data Generation

December 18, 2015

A unique opportunity exists to develop data literacy education for children born into a world shaped by big data.

The question of how growing up with digital technology shapes a generation’s outlook has fueled discussion since the description “Digital Natives” was coined in 2001. As commentators begin to weigh in on the experience of those born in the decade-and-a-half since then, Emmanuel Letouzé, director of Data-Pop Alliance, believes one milestone merits special consideration: The advent of Big Data.

“I say we are in the presence of a ‘historical data generation’ in two senses,” says Letouzé. “Because data are being generated at a pace and scale never seen before, and because the children born around 2010 are the first ‘data generation’ in history; the same way those born between 1995 and 2000 were the first Internet generation.” For Letouzé, this means that we have a unique opportunity to prepare this generation to navigate the major technological trends already shaping the era: “Most of them will learn to read and write code, but they should also be able to critically assess how, by whom, for what purposes data are collected and used. One need not become a ‘data scientist’ to have a voice in the age of data.”

As we’ve covered previously, ensuring that not just data scientists have a voice in the age of data is key to ensuring that Data for Good lives up to the promise of its name. A major current in discussions of Data for Good this year has been the importance of engaging and empowering populations whose data forms Data for Good initiatives. Letouzé has stressed that such concepts may not be very meaningful if they are not grounded in helping communities understand what data are and how they are being used.

This underlies Letouzé’s call for a robust definition of data literacy, combining both a grasp of technical fundamentals and critical reasoning about the ends to which they are put. He stresses that data literacy must be conceptualized in terms of “the desire and ability to constructively engage in society through and about data—that is, a conceptualization that isn’t only about providing technical skills.”

It’s a view echoed by Daniel Pedraza, data scientist at the data analytics firm Quid. “To succeed, it’s about developing the right mix of qualitative and quantitative skills. Teamwork, perseverance, the ability to conceptualize architectures and processes, along with statistical, inference and regression skills, are all critical.”

If the promise of expanding data literacy is clear, what resources can be used to achieve this goal—and to do so in such a way that data literacy doesn’t remain exclusive to a few? Today, the most readily apparent resources are those that address the technical side of what data literacy means.  For children, there are even interactive toys, like Codie, a robot that teaches kids the principles of coding, offering a hands-on way to develop understandings of foundational technological concepts. While having fun, kids use real programming patterns, like variables, loops, conditionals and step routines, to direct Codie’s behavior, thus giving them a basis on which to build understanding of the fundamentals of topics like machine learning.

Yet as far as fostering critical reasoning around the use of data is concerned, Letouzé argues there’s currently a comparative dearth of resources—a situation that requires remedy. “That will require investing in basic, primary education. There are also huge needs at the academic level around the world. Evaluations of existing ‘data science’ programs—online and offline—point to their too-narrow focus on technical aspects at the expense of ethical considerations notably.”

Still, Letouzé sees reason for optimism in building on the success of initiatives already under way. “Another last piece of the puzzle is raising awareness and building capacities among ‘policymakers.’  There is no simple fix; the recipe involves a mix of outreach and advocacy, trainings. One of the most effective means is to get diverse groups of professionals to meet and work together with data, ‘hands-on.’ It’s a slow process but things have changed fast in the past couple of years—overall in the right direction I think.”

This suggests that such gatherings can play an important role in building bridges between practitioners, policymakers and educators. It may well be that sustained dialogue among the three is critical to designing successful data literacy programs, given that data science and its social deployments are continuously evolving. “The field changes so quickly, it’s important to adapt and be able to design your own solutions,” says Pedraza. No doubt, an essential lesson for “generation data.”

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