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Showing 1 to 15 of 92 results Save | Export
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Robin Clausen – Grantee Submission, 2024
Early warning systems (EWS) using analytical tools that have been trained against prior years' data, can reliably predict dropout risk in individual students so that educators may intervene early to help avert this from happening. Risk profiles for dropouts aren't always useful since students often do not conform to the profiles. Researchers with…
Descriptors: Early Intervention, Predictor Variables, Potential Dropouts, At Risk Students
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Patricia Everaert; Evelien Opdecam; Hans van der Heijden – Accounting Education, 2024
In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…
Descriptors: Accounting, Business Education, Artificial Intelligence, College Freshmen
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Ormiston, Heather E.; Renshaw, Tyler L. – School Mental Health, 2023
Universal screening for social, emotional, and behavioral risk is an important method for identifying students in need of additional or targeted support (Eklund and Dowdy in School Mental Health 6:40-49, 2014). Research is needed to explore how potential bias may be implicated in universal screening. We investigated student demographics as…
Descriptors: Student Characteristics, Predictor Variables, At Risk Students, Student Placement
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Chuan Cai; Adam Fleischhacker – Journal of Educational Data Mining, 2024
We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…
Descriptors: College Students, Student Attrition, Dropouts, Potential Dropouts
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Lucia Uguina-Gadella; Iria Estevez-Ayres; Jesus Arias Fisteus; Carlos Alario-Hoyos; Carlos Delgado Kloos – IEEE Transactions on Learning Technologies, 2024
Students learn not only directly from their teachers and books, but also by using their computers, tablets, and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this article is based on gathering real-time events as students interact with learning tools and materials…
Descriptors: Predictor Variables, Academic Achievement, Computer Assisted Instruction, Electronic Learning
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Roberts, Nicola – Journal of Further and Higher Education, 2023
Globally, statistical analyses have found a range of variables that predict the odds of first-year students failing to progress at their Higher Education Institution (HEI). Some of these studies have included students from a range of disciplines. Yet despite the rise in the number of criminology students in HEIs in the UK, little statistical…
Descriptors: Predictor Variables, Academic Achievement, Academic Failure, College Freshmen
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D. V. D. S. Abeysinghe; M. S. D. Fernando – IAFOR Journal of Education, 2024
"Education is the key to success," one of the most heard motivational statements by all of us. People engage in education at different phases of our lives in various forms. Among them, university education plays a vital role in our academic and professional lives. During university education many undergraduates will face several…
Descriptors: Models, At Risk Students, Mentors, Undergraduate Students
Brian Holzman; Horace Duffy – Annenberg Institute for School Reform at Brown University, 2024
As states incorporate measures of college readiness into their accountability systems, school and district leaders need effective strategies to identify and support students at risk of not enrolling in college. Although there is an abundant literature on early warning indicators for high school dropout, fewer studies focus on indicators for…
Descriptors: College Enrollment, College Readiness, Educational Indicators, Predictor Variables
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Nalbone, David P.; Ashoori, Minoo; Fasanya, Bankole K.; Pelter, Michael W.; Rengstorf, Adam – International Journal for the Scholarship of Teaching and Learning, 2023
Much discussion in higher education has focused upon predicting student learning, and how to identify students who may be at particular risk of failure. Little research has actually tackled that challenge, and research on the scholarship of teaching and learning (SoTL) in this areas is scarce; this study does so by measuring students across three…
Descriptors: College Students, Predictor Variables, Academic Achievement, Identification
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Dixon, Chris; Oxley, Emily; Nash, Hannah; Gellert, Anna Steenberg – Journal of Learning Disabilities, 2023
Traditional static tests of reading and reading-related skills offer some ability to predict future reading performance, though such screeners may misclassify children with or at risk of reading disorder (RD). Dynamic assessment (DA) is an alternative approach that measures learning potential and may be less dependent on learning background. A…
Descriptors: Evaluation Methods, Identification, Reading Difficulties, Literature Reviews
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Jessica Paynter; Kate O'Leary; Marleen Westerveld – Journal of Autism and Developmental Disorders, 2024
We explored reading comprehension development in children on the spectrum from pre-school to the first (YOS1) and third year of schooling (YOS3). Children were first assessed on meaning-related skills in pre-school. Forty-one children completed follow-up assessments of reading comprehension, reading accuracy, and listening comprehension in YOS1.…
Descriptors: Autism Spectrum Disorders, Reading Comprehension, Predictor Variables, Preschool Children
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McClemont, Abbey J.; Morton, Hannah E.; Gillis, Jennifer M.; Romanczyk, Raymond G. – Journal of Autism and Developmental Disorders, 2021
Children with Autism Spectrum Disorder (ASD) or Attention-Deficit/Hyperactivity Disorder (ADHD) are at increased risk for bullying victimization. School refusal is a 'red flag' for identification of bullying in children with ASD and/or ADHD. This study examined the impact of diagnoses, demographics, and school variables on school refusal due to…
Descriptors: Predictor Variables, Attendance, Autism, Pervasive Developmental Disorders
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Splett, Joni W.; Brann, Kristy L.; Trainor, Kathryn M.; Shen, Zuchao – School Psychology, 2023
Along with increased attention to universal screening for identifying social, emotional, and behavioral (SEB) concerns is the need to ensure the psychometric adequacy of tools available. Nearly all extant tests of universal SEB screening validity focus on traditional inferential forms with little to no study of the consequences of actions…
Descriptors: Elementary School Students, Screening Tests, Social Problems, Emotional Problems
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Charles E. Jakobsche – Journal of Chemical Education, 2023
Our goal as educators should be to help our students become well positioned to achieve future success. To develop effective strategies for accomplishing this objective, we must first understand the root causes of success. Thus, to best serve undergraduate students who are taking organic chemistry courses, we must understand the attributes that…
Descriptors: Undergraduate Students, Undergraduate Study, Organic Chemistry, College Science
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Soland, James; Domingue, Benjamin; Lang, David – Teachers College Record, 2020
Background/Context: Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like…
Descriptors: Identification, At Risk Students, Potential Dropouts, High School Students
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