In recent years, learning process data have become increasingly easy to collect through computer-based learning environments. This has led to increased interest in the field of "learning analytics," which is concerned with leveraging learning process data in order to better understand, and ultimately to improve, teaching and learning. In computing education, the logical place to collect learning process data is through integrated development environments (IDEs), where computing students typically spend large amounts of time working on programming assignments. While the primary purpose of IDEs is to support computer programming, they might also be used as a mechanism for delivering learning interventions designed to enhance student learning. The possibility of using IDEs both to collect learning process data, and to strategically intervene in the learning process, suggests an exciting design space for computing education research: that of "IDE-based learning analytics." In order to facilitate the systematic exploration of this design space, we present an IDE-based data analytics process model with four primary activities: (1) "Collect data," (2) "Analyze data," (3) "Design intervention," and (4) "Deliver intervention." For each activity, we identify key design dimensions and review relevant computing education literature. To provide guidance on designing "effective" interventions, we describe four relevant learning theories, and consider their implications for design. Based on our review, we present a call-to-action for future research into IDE-based learning analytics.