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Home Resource Simple early warning system vs. computer algorithms: Does one more accurately identify students at near-term academic risk?
Simple early warning system vs. computer algorithms: Does one more accurately identify students at near-term academic risk?
By accurately identifying students at risk of near-term academic problems, districts can target services for these students to prevent problems before they lead to even more serious consequences, such as dropping out of school. A recent REL Mid-Atlantic study assessed two approaches for identifying students with near-term academic risk.
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ERIC Descriptors
At Risk Students, Attendance, Data Analysis, Data Use, Dropout Prevention, Dropout Research, Evidence Based Practice, Grades (Scholastic), Prediction, Predictive Measurement, Predictor Variables, Standardized Tests, Statistical Analysis, SuspensionResource Information
Mid-Atlantic | Resource Type: Infographic | Resource
Date: November 2021
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