Logged tracking data for online courses are generally not available to instructors, students, and course designers and developers, and even if these data were available, most content-oriented instructors do not have the skill set to analyze them. Learning analytics, mined from logged course data and usually presented in the form of learning dashboards, can inform instructors and students about how students are doing in a course, for example, questions answered correctly or incorrectly, pages visited, exercises completed, and assessment scores. However, these dashboards do not provide data on what students are doing when interacting with course materials. This dearth of information severely limits the amount of feedback that online course instructors can give to students on how to best maximize their time online to meet their learning outcomes. Using logged data from an online French course, the research presented here offers a sample of preliminary statistical visualizations created using data from students interacting with a course video and its attendant questions. By developing a methodology that can be applied to other similar datasets, these types of visualizations could be automatically generated for all stakeholders to obtain a fuller picture of how students behave in online courses.