The paper begin with some history – focusing on the anthropologist Claude Lévi-Strauss and his ideas about literacy being used as a weapon of those in power to ensure and educated work populace. We move into an argument about “literacy in the age of data” being a better way to start asking questions that “data literacy”. As I talk about often, we focus on how data should serve the purpose of greater social inclusion. This requires a focus on the words we use to talk about this stuff (is. “information” or “data”?). This is all built on a definition of data literacy that includes the “desire and ability to constructively engage in society through and about data”.
If you’re interested in some academic reading about the history and potential of this type of work, give it a read! It will be especially relevant to those trying to craft policies or programs that support building people’s capacity to work with data to create change.
When we talk about data science for good, collaborating with organizations that work for the social good, we are immediately entered into a conversation about empowerment. How can data science help these organizations empower their constituencies and create change in the world? Catherine and I are educators, and strongly believe learning is about empowerment, so this area naturally appeals to us! That’s why we wrote this paper for the Bloomberg Data for Good Exchange.
We’ve been thinking and working a lot on data literacy, and how to help folks build their capacity to work with information to create social change. We define “data literacy” as the ability to read, work with, analyze and argue with data. So how do we help build data literacy in creative and fun ways? One example is the activity we do around text analysis. We introduce folks to a simple word-couting website and give them lyrics of popular musicians to analyze. Over the course of half and hour folks poke at the data, looking for stories comparing word usage between artists. Then they sketch a visual to share a story.
Another example are my Data Murals – where we help a community group find a story in their data, collaboratively design a visual to tell that story, and paint it as a community mural.
This stuff is fun, and makes learning to work with data accessible. We focus on working with technical and non-technical audiences. The technical folks have a lot to learn about how to use data to effect change, while the non-technical folks want to build their skills to use data in support of their mission.
However this work has been focused on small data sets… when we think about “big data literacy” we see some gaps in our definition and our work. Here are four problems related to empowerment that we see in big data, related to our definition of data literacy:
lack of transparency: you can’t read the data if you don’t even know it exists
extractive collection: you can’t work with data if it isn’t available
technological complexity: you can’t analyze data unless you can overcome the technical challenges of big data
control of impact: you can’t argue for change with data unless you can effect that change
With these problems in mind, we decided we needed an expanded definition of “big data literacy”. This includes:
identifying when and where data is being collected
understanding the algorithmic manipulations
weighing the real and potential ethical impacts
So how do we work on building this type of big data literacy? First off we look to Freire for inspiration. We could go on for hours about his approach to building literacy in Brazil, but want to focus on his “Population Education”. That approach was about using literacy to do education and emancipation. This second piece matters when you are doing data for good; it isn’t just about acquiring technical skills!
We want to work with you on how to address this empowerment problem, and have a few ideas of our own that we want to try out. The paper has seven of these sketched out, but here are three examples.
Idea #1: Participatory Algorithmic Simulations
We want to create examples of participatory simulations for how algorithms function. Imagine a linear search being demonstrated by lining people up and going from left to right searching for someone named “Anita”. This would build on the rich tradition of moving your body to mimic and understand how a system functions (called “body syntonicity“). Participatory algorithmic simulations would focus on understanding algorithmic manipulations.
Ideas #2: Data Journals
Data can bee seen as the traces of the interactions between you and the world around you. With this definition in mind, in our classes we ask students to keep a journal of every piece of data they create during a 24 hour period (see some examples). This activity targets identifying when and where data is being collected. We facilitate a discussion about these journals, asking students which ones creep them out the most, which leads to a great chance to weigh the real and potential ethical implications.
Ideas #3: Reverse Engineering Algorithms:
We’ve seen a bunch of great work recently on reverse engineering algorithms, trying to understand why Amazon suggests certain products to you, or why you only see certain information on your Facebook. We think there are ways to bring this research to the personal level by designing experiments individuals can run to speculate about how these algorithms work. Building on Henry Jenkin’s idea of “Civic Imagination”, we could ask people to design how they would want the algorithms to work, and perhaps develop descriptive visual explanations of their own ideas.
We think each of these three can help build big data literacy and try to address big data’s empowerment problem. Read the paper for some other ideas. Do you have other ideas or experiences we can learn from? We’ll be working on some of these and look forward to collaborating!
Data-centric thinking is rapidly becoming vital to the way we work, communicate and understand in the 21st century. This has led to a proliferation of tools for novices that help them operate on data to clean, process, aggregate, and vi- sualize it. Unfortunately, these tools have been designed to support users rather than learners that are trying to develop strong data literacy. This paper outlines a basic definition of data literacy and uses it to analyze the tools in this space. Based on this analysis, we propose a set of pedagogical design principles to guide the development of tools and activities that help learners build data literacy. We outline a rationale for these tools to be strongly focused, well guided, very inviting, and highly expandable. Based on these principles, we offer an example of a tool and accom- panying activity that we created. Reviewing the tool as a case study, we outline design decisions that align it with our pedagogy. Discussing the activity that we led in aca- demic classroom settings with undergraduate and graduate students, we show how the sketches students created while using the tool reflect their adeptness with key data literacy skills based on our definition. With these early results in mind, we suggest that to better support the growing num- ber of people learning to read and speak with data, tool de- signers and educators must design from the start with these strong pedagogical principles in mind.
I recently re-read the report on Big Data, Communities and Ethical Resilience: A Framework for Action from the Rockefeller Foundation’s 2013 Bellagio/PopTech Fellows. Though kind of academic, It is well-worth your time (when you feel like getting head-y about this big data stuff). I particularly enjoyed and wanted to share this paragraph, because it is written more eloquently than I’m able to:
Of primary importance is to focus on people-centered, community-driven approaches. The discourse of big data and community resilience often excludes local participation by less powerful or technically literate populations. As a result, external experts may reduce complex social problems like community resilience to terms that are suited to technological solutions. This crowds out local knowledge, participation and agency, which undermines trust, social connectedness and resilience. Clear public policy and corporate governance frameworks are needed to foster a generative and inclusive environment that is conducive to local communities participating in their own data projects.