REL Mid-Atlantic study uses school and child welfare data to predict short term academic risks

Using school and child welfare data for Pennsylvania school districts, REL MidAtlantic finds that predictive models can be used to effectively identify at-risk students. They consider short-term academic outcomes including chronic absenteeism, suspensions, course failure, low grade point average, and low scores on state tests. The idea is to successfully identify near-term challenges so that administrators and school staff can provide additional support before a problem develops or a student considers dropping out. Interestingly, researchers found that models including out-of-school predictors from human services data did not enhance the performance of the models, suggesting models using only in-school data are sufficient. #education

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