Students using self-directed learning platforms, such as Duolingo, cannot be adequately assessed relying solely on responses to standard learning exercises due to a lack of control over learners' choices in how to utilize the platform: for example, how learners choose to sequence their studying and how much they choose to revisit old material. To provide accurate and well-controlled measurement of learner achievement, Duolingo developed two methods for injecting test items into the platform, which combined with Educational Data Mining techniques yield insights important for product development and curriculum design. We briefly discuss the unique characteristics and advantages of these two systems - Checkpoint Quiz and Review Exercises. We then present a case study investigating how different study approaches on Duolingo relate to learning outcomes as measured by these assessments. We demonstrate some of the unique benefits of these systems and show how educational data mining approaches are central to making use of this assessment data. [For the full proceedings, see ED615472.]