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Majdi Beseiso – TechTrends: Linking Research and Practice to Improve Learning, 2025
Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…
Descriptors: Prediction, Success, Academic Achievement, Artificial Intelligence
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Nathan Jones; Lindsey Kaler; Jessica Markham; Josefina Senese; Marcus A. Winters – Educational Researcher, 2025
Students with and without disabilities may be educated across various service delivery models (SDMs): general education, cotaught, pull-out, and self-contained. Still, evidence for their relative effectiveness at scale remains limited. Using longitudinal administrative data from Indiana, we measured the effect of different SDMs on test scores,…
Descriptors: Students with Disabilities, Teaching Methods, Students, Instructional Effectiveness
Nancy Montes; Fernanda Luna – UNESCO International Institute for Educational Planning, 2024
This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…
Descriptors: Data Collection, Data Use, At Risk Students, Foreign Countries
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Yu, L. C.; Lee, C. W.; Pan, H. I.; Chou, C. Y.; Chao, P. Y.; Chen, Z. H.; Tseng, S. F.; Chan, C. L.; Lai, K. R. – Journal of Computer Assisted Learning, 2018
This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information…
Descriptors: Prediction, Academic Failure, Models, Identification
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Coleman, Chad; Baker, Ryan S.; Stephenson, Shonte – International Educational Data Mining Society, 2019
Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…
Descriptors: Prediction, At Risk Students, Predictor Variables, Elementary Secondary Education
Merrill, Lisa; Siman, Nina; Wulach, Suzanne; Kang, David – Research Alliance for New York City Schools, 2015
iMentor's College Ready Program is a unique approach that combines elements of school-based mentoring, whole school reform, and technology in an effort to help students develop the full suite of knowledge, behaviors, and skills they need to complete high school and enroll and thrive in college. iMentor partners with high schools that serve…
Descriptors: Mentors, Educational Change, Technology Uses in Education, College Readiness