We identify three key areas where students articulated what they learned from working on the Dear Data project: data, identity, and design. With students' permission, we draw on the reflections they wrote about the project; these reflections accompanied the two data postcards that each student turned in. We also draw on IRB-approved video interviews with five students after the course was completed.


Data and its representations increasingly pervade academic and non-academic contexts as technologies enable a data-driven approach to many aspects of our daily lives. Indeed, it seems like the adjective "data-driven" is the equivalent now of "legitimate," "valid," the right way to gain knowledge and make decisions. But data-driven decisions aren't inherently better—rather, they are surely flawed—if we don't understand the data and the conclusions we can properly draw from it, and if we aren't able to factor in strengths and weaknesses of the data, its gaps and omissions, and the assumptions and biases underlying its collection. The Dear Data project offers one approach to cultivating this kind of awareness in students as an element of data literacy.

Although teaching data literacy is not the central task of a writing course, small data projects like this one can be incorporated into academic writing courses in a way that can also help advance related learning goals of critical thinking, engaged analysis, and clear communication, among others. After all, we are not trying to produce data scientists in our courses but rather capable readers, writers, students, and citizens—and, now more than ever, people who can ask and answer genuine questions with and about data. Annika Wolff, Daniel Gooch, Jose Cavero Montaner, Umar Rashid, and Gerd Kortuem (2016) described four types of data literate citizens who "would need to use data intelligently for solving real world problems": communicators who make sense of and tell stories about data for others; readers who interpret data they encounter in their every day lives; makers who use data to identify and solve real-world problems; and scientists who combine strong technical data skills with an in-depth knowledge of the domain of the data (p. 18). The Dear Data project touches on all of these roles and gives students an opportunity to experience for themselves a data-driven approach that doesn’t write humans out of the process.