Variables that address student enrollment patterns (e.g., persistence, enrollment inconsistency, completed credit hours, course credit load, course completion rate, procrastination) constitute a longstanding fixture of analytical strategies in educational research, particularly research that focuses on explaining variation in academic outcomes. However, nearly all measures of enrollment patterns are handicapped by untested assumptions about a more fundamental measure, namely students' rate of progress. In this paper, I first explain how a variety of widely used measures of enrollment patterns are inextricably linked to students' rate of progress. I then describe a method of modeling mathematically students' rate of progress that employs hierarchical (multilevel) discrete-time event history analysis of repeated events. I conclude with an empirical example of the application of this method in which I test several hypotheses concerning students' rate of progress through the remedial math sequence toward the outcome of college-level math competency. In addition to the utility of the method that is proposed here, the issues discussed in this paper have important practical implications for institutional research, particularly with respect to the use of the various measures of enrollment patterns to explain variation in students' attainment.