ABSTRACT
To study deep learning at scale requires the use of sophisticated technological tools and approaches to learning within a situated perspective. Rather than simply “mixing” methods, we need to understand how to think about empirical warrants in a way that uses the power of large-scale analyses to answer how and why questions: to use Big Data for thick description of deep learning. This chapter outlines the conceptual underpinnings of one such approach, quantitative ethnography, with the goal of using this particular methodology to examine critical considerations for integrating qualitative and quantitative techniques to assess deep learning using Big Data.
