It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the special section on learning analytics and learning theory, we describe some critical problems in the analysis of large-scale data that occur when theory is not involved. These questions revolve around what variables a researcher should attend to and how to interpret a multitude of micro-results and make them actionable. We conclude our comments with a discussion of how the collection of empirical papers included in the special section, and the commentaries that were invited on them, speak to these challenges, and in doing so represent important steps towards theory-informed and theory-contributing learning analytics work. Our ultimate goal is to provoke a critical dialogue in the field about the ways in which learning analytics research draws on and contributes to theory.