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IntroductionSelf-regulated learning skills are necessary for academic success. While not all students entering post-secondary education are proficient at many of these critical skills, they can be improved upon when practiced. However, self-regulation tends to be highly internal, making it difficult to measure. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong empirical inferences about a student's internal state. Automatically captured data traces also make it possible to provide automated interventions that help students practice and master self-regulated learning skills. Methods/resultsUsing an experimental methodology we created a set of promising data traces that are grounded in theory to study self-regulated learning within a typical Computer Science course. Extra attention is given to studying the skill of help-seeking, which is both a key to success in CS and requires unobtrusive observation to properly measure. DiscussionWe also make the case for taking a broader perspective with our data collection efforts. The traces identified in this paper are not from one source, but the full ecosystem of software tools common to CS courses.