The migration from traditional classrooms to online learning environments is in full effect. In the midst of these changes, a new approach to learning analytics needs to be considered. Learning analytics refers to the process of collecting and studying usage data in order to make instructional decisions that will support student success. In learning analytics, "usage data" can refer to a wide range of information being produced by the observed population. The necessary tools and technology used to study learning analytics are starting to become simplified, allowing librarians to develop a better understanding of their students learning habits. Rather than analyzing college students' general learning behavior, learning analytics can provide insight into the learning styles or patterns of a specific subset of students. Within learning analytics, the "learning process is assessed more so than final learning outcomes." Therefore, learning analytics involves a redesign of assessment that traditionally focuses on outcomes. This in-process assessment draws its data from the daily learning activity of students within their social and informational networks. The author discusses three interactive components to be studied when collecting data for learning analytics: (1) timing; (2) location; and (3) population. It is important to note that there is a sense of immediacy to these components; current data are very valuable in regard to learning analytics and in-process assessment. The first step in collecting data is to investigate these components as they relate to a group of students. Ultimately, a big picture will begin to develop about the daily learning activity of students within their network of courses.