The internet has had a vast and pervasive effect on many industries. It has resulted in the creation of new industries and has overhauled the dynamics that governed existing industries. One of the most traditional industries that is now struggling to cope with the changes brought on by the internet is the industry of higher education. Students can now access resources that they previously had no access to, and can enroll in courses that were previously out of reach to them. To cope with these changes, universities are more and more incorporating the use of learning management systems (LMS) in traditional face-to-face courses. So far, these systems have been primarily used as communication tools, that is, posting announcements for students, sharing slides and articles, and sending group emails. This case illustrates how student login data collected by these systems can be used by instructors to predict student performance, thus enabling instructors to identify low-performing students before the end of the semester when it is too late. Since the variable of interest is how much time passes between two consecutive logins for each student, I use a statistical technique called event-history analysis or survival analysis. Event history analysis is used to analyze the time to the occurrence of an event. It involves calculating the probability that an individual will log into the system at a certain time, in addition to finding the factors that might either increase or decrease this probability. The case study will explain the basic premise of this technique, and when it can be used. [This content is provided in the format of an e-book.]