UCLA develops and uses a learning management system to provide an online environment for students to access and interact with course content. The data collected by the learning management system is a direct measure of student activity, and provides information that augments known information about the student, such as assignment grades and demographics. This paper assesses UCLA’s learning management system data for its usefulness in creating an early warning system that will advise instructors and students of whether a student is likely to receive a failing grade. The data are used in two analyses: an exploratory analysis of how students use the learning management system, and a predictive model to
forecast end-of-term grades based on partial-term information. Recommendations on how to generalize the results across the UCLA undergraduate population are drawn from the findings of the analyses.