In student modeling, the concept of "mastery learning" i.e. that a student continues to learn a skill till mastery is attained is important. Usually, mastery is defined in terms of most recent student performance. This is also the case with models such as Knowledge Tracing which estimate knowledge solely based on patterns of questions a student gets correct and the task usually is to predict immediate next action of the student. In retrospect however, it is not clear if this is a good definition of mastery since it is perhaps more useful to focus more on student retention over a longer period of time. This paper improves a recently introduced model by Wang and Beck that predicts long term student performance by clustering the students and generating multiple predictions by using a recently developed ensemble technique. Another contribution is that we introduce a novel clustering algorithm we call "Regularity Clustering" and show that it is superior in the task of predicting student retention over more popular techniques such as k-means and Spectral Clustering.