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Marissa J. Filderman; Christy R. Austin – Beyond Behavior, 2024
Students with and at risk for emotional and behavioral disorders (EBD) struggle to acquire and develop writing skills. To support their students' unique needs, it is important for teachers to monitor student writing progress to make instructional decisions based on data. In this article we describe methods for progress monitoring focused on…
Descriptors: Emotional Disturbances, Behavior Disorders, At Risk Students, Writing Skills
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Khan, Ijaz; Ahmad, Abdul Rahim; Jabeur, Nafaa; Mahdi, Mohammed Najah – Smart Learning Environments, 2021
A major problem an instructor experiences is the systematic monitoring of students' academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to…
Descriptors: Artificial Intelligence, Academic Achievement, Progress Monitoring, Data Collection
Chad J. Coleman – ProQuest LLC, 2021
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 both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Elementary Secondary Education
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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Ethan R. Van Norman; Emily R. Forcht – Journal of Education for Students Placed at Risk, 2024
This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…
Descriptors: Data Collection, Predictive Measurement, Predictive Validity, Predictor Variables
Office of Inspector General, US Department of Education, 2023
The objective of this inspection was to determine what steps the Office of Special Education and Rehabilitative Services (OSERS) has taken to implement its final regulations on significant disproportionality in special education. The inspection found that OSERS provided general guidance and technical assistance for State educational agencies…
Descriptors: Students with Disabilities, Federal Legislation, Equal Education, Educational Legislation
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Thiry, Heather; Zahner, Dana Holland; Weston, Timothy; Harper, Raquel; Loshbaugh, Heidi – Change: The Magazine of Higher Learning, 2023
Vertical transfer from community college to a university offers a promising, although unrealized, pathway to diversify STEM disciplines. Studying how successful transfer-­receiving universities support STEM transfer students can offer insights into the institutional practices that promote transfer student retention and success. Using institutional…
Descriptors: College Transfer Students, STEM Education, College Role, Student Needs
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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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Bruhn, Allison L.; McDaniel, Sara C.; Rila, Ashley; Estrapala, Sara – Beyond Behavior, 2018
Students who are at risk for or show low-intensity behavioral problems may need targeted, Tier 2 interventions. Often, Tier 2 problem-solving teams are charged with monitoring student responsiveness to intervention. This process may be difficult for those who are not trained in data collection and analysis procedures. To aid practitioners in these…
Descriptors: Progress Monitoring, Behavior Problems, Student Behavior, At Risk Students
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January, Stacy-Ann A.; Van Norman, Ethan R.; Christ, Theodore J.; Ardoin, Scott P.; Eckert, Tanya L.; White, Mary Jane – School Psychology Review, 2018
The present study examined the utility of two progress monitoring assessment schedules (bimonthly and monthly) as alternatives to monitoring once weekly with curriculum-based measurement in reading (CBM-R). General education students (N = 93) in Grades 2-4 who were at risk for reading difficulties but not yet receiving special education services…
Descriptors: Progress Monitoring, Reading Improvement, Reading Tests, Student Evaluation
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Cumming, Therese M.; O'Neill, Sue C. – Intervention in School and Clinic, 2019
Students receiving behavioral supports in the third tier of the schoolwide positive behavioral interventions and supports (SWPBIS) framework are often identified as having emotional and behavior disabilities. Although educators implement evidence-based practices with fidelity, these practices are not always effective in supporting students with…
Descriptors: Data Use, Behavior Disorders, Emotional Disturbances, Intervention
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Filderman, Marissa J.; Austin, Christy R.; Toste, Jessica R. – Intervention in School and Clinic, 2019
The process of implementing intensive reading interventions using data-based decision-making (DBDM) becomes increasingly challenging as students move into the secondary grades and reading tasks correspondingly become more complex. This article provides teachers with guidelines to support effective implementation of DBDM for students with or at…
Descriptors: Data Use, Reading Difficulties, At Risk Students, Secondary School Teachers
Miller, Cynthia; Cohen, Benjamin; Yang, Edith; Pellegrino, Lauren – MDRC, 2020
College students have a better chance of succeeding in school when they receive high-quality advising. High-quality advising, when characterized by frequent communications between advisers and students, early outreach to students showing signs of academic or nonacademic struggles, and personalized guidance that addresses individual student needs,…
Descriptors: College Students, Academic Advising, Technology Uses in Education, Faculty Advisers
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Kostyo, Stephen; Cardichon, Jessica; Darling-Hammond, Linda – Learning Policy Institute, 2018
This policy brief is part of a larger research report, "Making ESSA's Equity Promise Real: State Strategies to Close the Opportunity Gap," that describes how states are using opportunities in ESSA [Every Student Succeeds Act] to better support historically underserved students through the thoughtful selection of specific equity measures…
Descriptors: Accountability, State Standards, Equal Education, Suspension
Mehrotra, Sarah; Morgan, Ivy S.; Socol, Allison – Education Trust, 2021
While new teachers bring energy and passion into their classrooms and schools, they can find themselves incredibly challenged as they learn how to plan and implement lessons, collect and use data to inform their instructional practices, build relationships with students and families, manage classroom behavior, and meet the varying academic,…
Descriptors: African American Students, Experienced Teachers, Educational Quality, Teacher Competencies
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