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Çelik, Cemal; Kartal, Hülya – International Online Journal of Primary Education, 2023
The aim of this study is to investigate the causes of reading problems experienced by third-grade students because of the instructional malpractices in education and develop a modeling with artificial neural networks. It was carried out according to the exploratory sequential model and consisted of two stages. In the qualitative part, a data pool…
Descriptors: Reading Difficulties, Models, Elementary School Students, Artificial Intelligence
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Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
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Matta, Tyler H.; Soland, James – Journal of Educational and Behavioral Statistics, 2019
The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random…
Descriptors: English Language Learners, Language Proficiency, Classification, Language Fluency
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Kiperman, Sarah; Black, Mary S.; McGill, Tia M.; Harrell-Williams, Leigh M.; Kamphaus, Randy W. – Journal of Psychoeducational Assessment, 2014
This study assesses the ability of a brief screening form, the Behavioral and Emotional Screening System-Student Form (BESS-SF), to predict scores on the much longer form from which it was derived: the Behavior Assessment System for Children-Second Edition Self-Report of Personality-Child Form (BASC-2-SRP-C). The present study replicates a former…
Descriptors: Screening Tests, Prediction, Elementary School Students, Grade 3
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Martin, Rebecca B.; Cirino, Paul T.; Barnes, Marcia A.; Ewing-Cobbs, Linda; Fuchs, Lynn S.; Stuebing, Karla K.; Fletcher, Jack M. – Journal of Learning Disabilities, 2013
The present study evaluated the stability of math learning difficulties over a 2-year period and investigated several factors that might influence this stability (categorical vs. continuous change, liberal vs. conservative cut point, broad vs. specific math assessment); the prediction of math performance over time and by performance level was also…
Descriptors: Mathematics Instruction, Mathematics Skills, Learning Problems, Longitudinal Studies
Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
In this technical report, we document the results of a cross-validation study designed to identify optimal cut-scores for the use of the easyCBM[R] mathematics test in the state of Washington. A large sample, randomly split into two groups of roughly equal size, was used for this study. Students' performance classification on the Washington state…
Descriptors: Testing Programs, Mathematics Tests, Prediction, Measurement Techniques
Park, Bitnara Jasmine; Anderson, Daniel; Irvin, P. Shawn; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
Within a response to intervention (RTI) framework, students are typically identified as "academically at-risk" if they score below a specified cut-point on a benchmark screener. Students identified as at-risk are provided with an intervention intended to increase achievement. In the following technical report, we describe a process for…
Descriptors: Intervention, Diagnostic Tests, Response to Intervention, At Risk Students
Irvin, P. Shawn; Park, Bitnara Jasmine; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Washington state. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 900 students per grade, randomly split into two…
Descriptors: Intervention, Diagnostic Tests, Response to Intervention, At Risk Students
Park, Bitnara Jasmine; Irvin, P. Shawn; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Oregon. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 2,000 students per grade, randomly split into two groups of…
Descriptors: Testing Programs, Reading Tests, Prediction, Measurement Techniques