Identifying enrollment patterns associated with course success can help educators design better degree plans, and students make informed decisions about future enrollments. While discriminating pattern mining techniques can be used to address this problem, course enrollment patterns include sequence and quantity (grades) information. None of the existing methods were designed to account for both factors. In this work we present UPM, a Universal discriminating Pattern Mining framework that simultaneously mines various types of enrollment patterns while accounting for sequence and quantity using an expansion-specific approach. Unlike the existing methods, UPM expands a given pattern with an item by finding a minimum-entropy split over the item's quantities. We then use UPM to extract discriminating enrollment patterns from the high and the low performing student groups. These patterns can be utilized by educators for degree planning. To evaluate the quality of the extracted patterns, we adopt a supervised classification approach where we apply various classification techniques to label students according tho their performance based on the extracted patterns. Our evaluation shows that the classification accuracies obtained using the UPM extracted patterns are higher than the accuracies obtained using patterns extracted by other techniques. Accuracy improves significantly for students with larger numbers of patterns. Moreover, expansion-specific quantitative mining leads to more accurate classifications than the methods that do not account for quantities (grades).