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Christie, 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
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Hwang, Gwo-Jen; Panjaburee, Patcharin; Triampo, Wannapong; Shih, Bo-Ying – British Journal of Educational Technology, 2013
Diagnosing student learning barriers has been recognized as the most fundamental and important issue for improving the learning achievements of students. In the past decade, several learning diagnosis approaches have been proposed based on the concept-effect relationship (CER) model. However, past studies have shown that the effectiveness of this…
Descriptors: Mathematics, Learning Problems, Models, Concept Mapping