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The authors propose using an ensemble of analytic models that considers cost as a better form of analysis that can be used as input to decision support systems to inform decision makers and help them choose intervention methods. This article uses ten years of data for 10,000 students and applies ten analytical models to find the best predictor of at-risk students. 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