ABSTRACT
The advent of computational tools for learning and assessment and the new field of data science presents the opportunity to examine processes of learning at scale. One critical feature of the learning sciences, however, is that researchers conduct analyses from theoretical perspectives that are designed to investigate how learning happens in the real world of educational practice. Qualitative methods are particularly appropriate for addressing such process questions However, qualitative analyses are typically done by hand. This chapter describes epistemic network analysis (ENA), a network analysis technique that uses statistical and computational techniques to bring qualitative insights to bear on large corpuses of data. ENA is based on epistemic frame theory, which looks at how learning is simultaneously embedded in culture, discourse, interaction, and time. As a result, ENA analyses of large data sets produce models that are not only about what teachers and students do, but how of how they make meaning within a culture of learning.
