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Davis, Marcia H. – Journal of Education for Students Placed at Risk, 2023
U.S. Department of Education research indicates that early warning indicator systems are being used in at least half of high schools in the United States. Previous findings from an efficacy study of one early warning indicator and response system, the Early Warning Indicator (EWI) team model, indicated that ninth grade students in schools using…
Descriptors: High Schools, High School Students, Grade 9, Dropout Prevention
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Cattell, Lindsay; Bruch, Julie – Regional Educational Laboratory Mid-Atlantic, 2021
This report provides information for administrators in local education agencies who are considering early warning systems to identify at-risk students. Districts use early warning systems to target resources to the most at-risk students and intervene before students drop out. Schools want to ensure the early warning system accurately identifies…
Descriptors: At Risk Students, Identification, Artificial Intelligence, Dropout Prevention
National Forum on Education Statistics, 2018
The Forum Guide to Early Warning Systems provides information and best practices to help education agencies plan, develop, implement, and use an early warning system in their agency to inform interventions that improve student outcomes. The document includes a review of early warning systems and their use in education agencies and explains the…
Descriptors: Educational Indicators, Best Practices, Elementary Secondary Education, Data Collection
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Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
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Young, Ellie L.; Moulton, Sara E.; Julian, Alex – Preventing School Failure, 2021
The authors compared data from a social-emotional-behavior screener (i.e., the "Student Risk Screening Scale--Internalizing and Externalizing" [SRSS-IE]) with data from a high school early warning system. Data from 2,256 suburban high school students were used. Research questions examined the degree to which student demographic variables…
Descriptors: Student Behavior, Social Development, Emotional Development, At Risk Persons
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Sletten, Mira Aaboen; Tøge, Anne Grete; Malmberg-Heimonen, Ira – Scandinavian Journal of Educational Research, 2023
This cluster-randomised study investigated the effects of a Norwegian early warning system, the IKO model. IKO is a Norwegian acronym for identification, assessment, and follow-up, and the model aims to improve schools' abilities to identify and support students who are at risk of dropping out during the school year. The study involved 7677…
Descriptors: Attendance, Comparative Analysis, Secondary School Students, Foreign Countries
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Faria, Ann-Marie; Sorensen, Nicholas; Heppen, Jessica; Bowdon, Jill; Eisner, Ryan – Society for Research on Educational Effectiveness, 2018
The national high school graduation rate reached its highest level in U.S. history--82 percent--during the 2013-14 school year (Kena et al., 2016)--but dropout remains a persistent problem in the Midwest and nationally. Early warning systems that use research-based warning signs to identify students at risk of dropping out have emerged as one…
Descriptors: Progress Monitoring, At Risk Students, Graduation Rate, Program Effectiveness
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Plak, Simone; Cornelisz, Ilja; Meeter, Martijn; van Klaveren, Chris – Higher Education Quarterly, 2022
Early Warning Systems (EWS) in higher education accommodate student counsellors by identifying at-risk students and allow them to intervene in a timely manner to prevent student dropout. This study evaluates an EWS that shares student-specific risk information with student counsellors, which was implemented at a large Dutch university. A…
Descriptors: At Risk Students, Identification, Counseling, Foreign Countries
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Fox, Joanna; Balfanz, Robert – Teachers College Record, 2020
Background/Context: Over the past decade early warning systems which use predictive indicators to identify students in need of additional supports to stay on track to high school graduation have spread from a few schools to most states. There is now a growing interest in extending the utility of early warning systems from high school graduation to…
Descriptors: Identification, High School Graduates, Postsecondary Education, College Readiness
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Cano, Alberto; Leonard, John D. – IEEE Transactions on Learning Technologies, 2019
Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons…
Descriptors: Progress Monitoring, At Risk Students, Disproportionate Representation, Underachievement
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Cassells, Laetitia – Assessment & Evaluation in Higher Education, 2018
The application of formative assessment principles in higher education has become increasingly important in South Africa. In this case study the researcher assesses the effectiveness of the application of an early warning system to the higher-education environment in a high failure rate subject. This method is applied according to recommended…
Descriptors: Identification, At Risk Students, Higher Education, Foreign Countries
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Hu, Yung-Hsiang – International Review of Research in Open and Distributed Learning, 2022
Early warning systems (EWSs) have been successfully used in online classes, especially in massive open online courses, where it is nearly impossible for students to interact face-to-face with their teachers. Although teachers in higher education institutions typically have smaller class sizes, they also face the challenge of being unable to have…
Descriptors: Dropout Prevention, At Risk Students, Online Courses, Private Colleges
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Hayat Sahlaoui; El Arbi Abdellaoui Alaoui; Said Agoujil; Anand Nayyar – Education and Information Technologies, 2024
Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…
Descriptors: Sampling, Classification, Algorithms, Prediction
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Lesh, Jennifer J.; Roberts, Cortney; Cavitt, Dennis; Morales, Diana L. – NASSP Bulletin, 2021
MTSS promises a school-wide early warning system, high-quality instruction, and evidence-based interventions. However, research has focused mainly on the elementary level. This study examined the beliefs and perceptions of over 300 administrators and teachers currently implementing MTSS in secondary schools using survey research. Results showed…
Descriptors: Urban Schools, Secondary Schools, Administrators, Secondary School Teachers
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Xu, Shun; Liu, Meixin; Ma, Dan – Computers in the Schools, 2023
Improving the digital citizenship of secondary vocational students is critical for cultivating vocational talents in education. It is an inevitable requirement for modernizing vocational education. Focusing on social media competence, this study explores possible ways to improve the digital citizenship of secondary vocational students. A total of…
Descriptors: Secondary School Students, Vocational Education, Digital Literacy, Social Media
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