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Loren Lydia Baranko Faught – ProQuest LLC, 2023
Early intervention is a method institutions use to identify and support students who are having academic difficulty and might be designated as "at-risk", or more likely to leave an institution (Villano et al., 2018). Institutions often adopt early alert systems to support early intervention efforts and student retention (Barefoot et al.,…
Descriptors: Intervention, At Risk Students, Progress Monitoring, Program Implementation
Fatima, Saba – ProQuest LLC, 2023
Predicting students' performance to identify which students are at risk of receiving a D/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among…
Descriptors: Artificial Intelligence, Prediction, Outcomes of Education, At Risk Students
Sönmez, Selami – Universal Journal of Educational Research, 2018
Descartes expresses his opinion on the method very clear with the quote: "The whole secret of the method; starting from the circle and gradually going up the steps to the most complicated ". When it is thought that the knowledge of the absolute and unchanging truth in the positive sciences has not yet been reached, it should not be…
Descriptors: Scientific Research, Research Methodology, Classification, Prediction
David M. Alexandro – ProQuest LLC, 2018
In response to the high school dropout crisis, which comes with great economic and social costs, early warning systems (EWSs) have been developed to systematically predict and improve student outcomes. The purpose of this study is to evaluate different statistical and machine learning methods to predict high school student performance and improve…
Descriptors: At Risk Students, Progress Monitoring, Artificial Intelligence, Prediction
Parker, David C.; Van Norman, Ethan; Nelson, Peter M. – Learning Disabilities Research & Practice, 2018
The accuracy of decision rules for progress monitoring data is influenced by multiple factors. This study examined the accuracy of decision rule recommendations with over 4,500 second-and third-grade students receiving a tier II reading intervention program. The sensitivity and specificity of three decision rule recommendations for predicting…
Descriptors: Progress Monitoring, Accuracy, Grade 2, Grade 3
Parson, Lorien – ProQuest LLC, 2012
An existing data set for a sample of 3rd grade students was used to determine the relationship between performance during a reading intervention and short-term achievement test outcomes, and long-term risk status. Students participated in a reading intervention, one-on-one practice with a trained adult, during which weekly curriculum based…
Descriptors: Grade 3, Reading Instruction, Intervention, Elementary School Students
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring