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Making Sense of Big Social Data

Big Social Data

Peppo Valetto and I have been working together on what is now called Big Social Data for four years at Drexel; exploring software engineering and technology mediated learning environments. My students, Christopher Mascaro and Alan Black have been focused on the study of Big Social Data in political discourse and culture. Nora McDonald, a new student I am working with, has studied both cultural phenomena on Twitter and virtual organizations on Github with Peppo, Kelly Blincoe and I. Together, we have begun to understand and articulate the ontological, methodological, and theoretical challenges of making sense of Big Social Data. Computation is a critical element of our work; but it is secondary to the theories related to learning, coordination, discourse, information and knowledge construction that we use to frame *how* we make sense of "big social data".

My long standing passion is to have the empirical studies I do inform design. My work with Gerry Stahl, Carolyn Rose and others at the Math Forum at Drexel inform this way of thinking about "Big Social Data" by incorporating both pedagogical and learning technology design. Principally through Virtual Math Teams and more recently through new collaborations I am beginning with the Math Forum, focused on identifying levels of interest in mathematics as defined by Renninger.

Big Social Data for Design and Learning

From a design perspective, "big social data" is a way to frame how we make sense of the rich electronic trace data (logs) that are left behind in various learning environments; and then to use those logs to provide useful feedback to people about the learning that is taking place. Partly this is a social theory of learning framing, which is what my 2009 dissertation examines. In the four years since that once in a lifetime (for me) project, the scope of my inquiry has expanded to fields of software engineering, social media, computational linguistics, "big data" and virtual organizations. Furthermore, I have taken up projects focused on workplace learning and rural outsourcing firms that train their own workers. Why? Simply put, there is not enough signal solely in the study of how people learn in traditional, school focused contexts to identify the design ideas that are likely to push learning technology design forward to a breakthrough.

There are also institutional obstacles to the application of data science to learning that arise from learning technologies that are already in place. In one widely rejected article I wrote with Jonathan Foster, and which we each abandoned for different reasons, I argue that a fundamental issue in the slow market innovation of novel learning technologies centers on infrastructure. Not the first level technical infrastructure, or the second level process and interaction focused infrastructure, but infrastructuring around resource allocation within educational institutions. To date, we experience specific difficulties in trying new things at an institutional level because the human and capital costs to change are prohibitive. It takes time to turn a big boat, even at sea.

Given the institutional limitations, and my own ambitions for advancing learning technology through the systematic study of how people interact, form groups and build institutions in a virtual world, I have spent a number of years examining contexts outside formal learning environments. The work that is published and in progress brings a novel and I think important perspective to learning technology design. A perspective born of the deep study of how people learn in informal contexts; how non-learning focused virtual organizations function; and how people use social media to argue, share information and build connection in ways that we frankly do not yet understand.

I think one key, as it relates to "Big Social Data", is that if you want to understand learning and make learning technology better, you have to study how people learn. And that's mostly not in school. If you want to build better learning technology, you have to study technology people use and how they use it. And that's mostly not Blackboard or systems like it. IMHO.