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Showing all 13 results Save | Export
Zachary Weingarten; Paul K. Steinle – National Center on Intensive Intervention, 2023
Data-based individualization (DBI) is a systematic approach to intensifying and individualizing interventions for students who require more support. Diagnostic data represent the third step in the DBI process. When progress monitoring data indicate that a student is not making adequate progress in an intervention, educators use diagnostic data to…
Descriptors: Data Use, Student Needs, Intervention, Individualized Instruction
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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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
Diana Louise Nestico-Arnold – ProQuest LLC, 2023
Elementary school teachers struggle to collect and use reading progress data to make instructional decisions. Collecting and interpreting reading progress data when making instructional decisions allows districts to identify and support students who need early reading intervention and may protect districts from Free Appropriate Public Education…
Descriptors: Progress Monitoring, Reading Achievement, Data Use, Decision Making
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Francis, Grace L.; Duke, Jodi M.; Fujita, Megan; Raines, Alexandra – TEACHING Exceptional Children, 2021
Adolescents with disabilities experience co-occurring mental health needs at higher rates than their peers without disabilities (Blake, 2017; Milligan et al., 2015; Poppen et al., 2016; Thornton et al., 2017). Mental health needs often become more prominent as individuals with disabilities transition from childhood to adolescence (White et al.,…
Descriptors: Adolescents, Mental Health, Wellness, Comorbidity
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Filderman, Marissa J.; Toste, Jessica R. – TEACHING Exceptional Children, 2018
Reading proficiency is fundamental to school success. However, up to 50% of students with reading disabilities are not making adequate progress. Students who demonstrate persistent and severe reading difficulties require increasingly intensive instruction individualized to meet their instructional needs Individualizing instruction with…
Descriptors: Reading Difficulties, Reading Skills, Individualized Instruction, Decision Making
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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
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
Mandinach, Ellen B.; Miskell, Ryan C. – WestEd, 2017
In the summer of 2015, The Ewing Marion Kauffman Foundation released an education data tool, EdWise, that made publically available much of the data about public education that had been released by the Missouri Department of Elementary and Secondary Education (DESE). EdWise is a rich source of aggregate, grade level information on student…
Descriptors: Focus Groups, Stakeholders, Parents, Public Education
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Kern, Lee; Wehby, Joseph H. – TEACHING Exceptional Children, 2014
In an earlier article (EJ1058920), Lee Kern and Joseph H. Wehby identified the reasons and process for using adaptive intensive behavioral intervention. Kern and Wehby use this article to present a fictional example of how the intervention is applied. Isaac, a 12 year old, 7th grade student at Highland Middle School, had a history of behavior…
Descriptors: Data Collection, Behavior Problems, Behavior Modification, Intervention
Kenyatta, Candace – Region 8 Comprehensive Center, 2021
The Diversifying the Education Profession Ohio Taskforce consists of aspiring teachers, K-12 educators, human resources personnel, educator preparation program representatives, community members, State Board of Education representation, and staff members from the Ohio Department of Education and Ohio Department of Higher Education and convened to…
Descriptors: Student Needs, Student Diversity, Elementary Secondary Education, Faculty Development
National Forum on Education Statistics, 2015
The National Forum on Education Statistics (Forum) organized the College and Career Ready (CCR) Working Group to explore how state and local education agencies (SEAs and LEAs) can use data to support college and career readiness initiatives. The working group determined that high-quality data in integrated K12, postsecondary, and workforce data…
Descriptors: Data Analysis, Data Collection, College Readiness, Career Readiness