Education systems are increasingly creating rich, longitudinal data sets with frequent, and even real-time, data updates of many student measures, including daily attendance, homework submissions, and exam scores. These data sets provide an opportunity for district and school staff members to move beyond an indicators-based approach and instead employ new methods to compute more frequent, more accurate, and more nuanced predictions of student risk. For these reasons, MDRC has been exploring the value of predictive modeling. Predictive modeling estimates individuals' future outcomes or their probabilities of future outcomes. It does this by building and testing a model using data on similar individuals whose outcomes are already known. When results are interpreted correctly, predictive modeling offers benefits to those engaged in continuous improvement efforts and those who are looking to allocate resources more efficiently. MDRC researchers have developed and implemented a comprehensive predictive modeling framework that allows for rapid and iterative estimation of a "continuous" measure of risk (a probability between 0 and 1 of not achieving an outcome) for each student at a point in time. Such estimates allow students to be ranked by their risk levels, and thresholds can be determined for particular interventions.