Between Platform and Domain: The Politics of Knowledge Production in the World of MOOCs / Dr. Shreeharsh Kelkar (University of California, Berkeley)
Abstract: The new field of data science has always been caught up in thorny questions of expert jurisdiction, commonly articulated in the form of its Other, the “domain” a site of particularity that lends itself to the supposedly universal data scientific method. Some scholars have suggested that the jurisdictional contest between data scientists and domain experts emerges when data scientists go out "prospecting," i.e. when they discover (or invent) the disorder that exists in a new domain, and frame the data scientific method as a solution to that disorder. This paper argues instead that the uptake of data-driven methods across institutions is not so much about data scientists prospecting as it is about reformers re-imagining the function of their institutions through the lens of digital platforms. I demonstrate this through a two-year ethnographic study of educational experts working with Massive Open Online Courses or MOOCs who drew on contemporary online platforms in their project of reinventing educational institutions and knowledge production. These educational reformers came from three different camps: engineers, online learning experts, and learning scientists. Each group saw the value and purpose of data-driven methods differently based on their objectives. Engineers and learning scientists imagined learners as “users” of platforms, to be governed through data analysis and nudging, while online learning experts saw learners as autonomous members of virtual communities. Engineers viewed the instructor as yet another “user” while learning scientists saw them as “domain experts.” Rather than one group pushing “data science” into the domain, there were many varieties of data science operating corresponding to different ways of imagining educational institutions as platforms. By emphasizing how reformers “pull” data scientific methods into domains, we can begin to understand both the proliferation of data science across institutions as well as the complicated dynamic between data scientists and domain experts.
Bio: Shreeharsh Kelkar is a Lecturer in Interdisciplinary Studies at the University of California, Berkeley. His research describes how new assemblages of algorithms, big data, and artificial intelligence (AI) are changing work practices, expertise, and knowledge production. His book manuscript, “Reinventing Expertise: Technology Reformers and the Platformization of Higher Education,” investigates how technology reformers and their regime of knowledge practices are re-configuring what it means to be an educational expert.
Sunday, June 6, 18:00