This article discusses two types of opportunities for interdisciplinary collaboration between computational linguistics (CL) and language learning research. We target the connection between data and theory in second language (L2) research and highlight opportunities to (a) enrich the options for obtaining data and (b) support the identification and valid interpretation of relevant learner data. We first characterize options, limitations, and potential for obtaining rich data on learning: from Web-based intervention studies supporting the collection of experimentally controlled data to online workbooks facilitating large-scale, longitudinal corpus collection for a range of learning tasks and proficiency levels. We then turn to the question of how corpus data can systematically be used for L2 research, focusing on the central role that linguistic corpus annotation plays in that regard. We show that learner language poses particular challenges to human and CL analysis and requires more interdisciplinary discussion of analysis frameworks and advances in annotation schemes.