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Download full textChristie, S. Thomas; Jarratt, Daniel C.; Olson, Lukas A.; Taijala, Taavi T. – International Educational Data Mining Society, 2019
Schools across the United States suffer from low on-time graduation rates. Targeted interventions help at-risk students meet graduation requirements in a timely manner, but identifying these students takes time and practice, as warning signs are often context-specific and reflected in a combination of attendance, social, and academic signals…
Descriptors: Dropout Prevention, At Risk Students, Artificial Intelligence, Decision Support Systems
