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Showing 1 to 15 of 33 results Save | Export
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Soyoung Park; Pamela M. Stecker; Sarah R. Powell – Intervention in School and Clinic, 2024
This article provides teachers with a toolkit for assessing students in the context of data-based individualization (DBI) in mathematics. Assessing students is a critical component of DBI because it provides teachers with information about what they may need to modify in their instructional programs. In this article, we provide teachers with…
Descriptors: Student Evaluation, Individualized Instruction, Mathematics Instruction, Progress Monitoring
Data Quality Campaign, 2024
Recent data from statewide assessments, scores on the National Assessment of Educational Progress (NAEP), and college remediation needs show that an increasing number of K-12 students are not performing at grade level. As schools look to support these students' learning, some districts are turning to a proven strategy for identifying the students…
Descriptors: National Competency Tests, Academic Achievement, Elementary Secondary Education, At Risk Students
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
Randhir Rawatlal; Rubby Dhunpath – Association for Institutional Research, 2023
Although student advising is known to improve student success, its application is often inadequate in institutions that are resource constrained. Given recent advances in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT), automated approaches such as the AutoScholar Advisor system affords viable alternatives to…
Descriptors: Academic Advising, Technology Uses in Education, Artificial Intelligence, Progress Monitoring
Marissa J. Filderman; Clark McKown; Pamela Bailey; Gregory J. Benner; Keith Smolkowski – Beyond Behavior, 2023
The collection of student data through screening and progress monitoring of social and emotional learning (SEL) skills is just as important as the implementation of curriculum and practices. Monitoring skill acquisition allows teachers to identify effective practices, provide intervention, and intensify support for students who need it. In this…
Descriptors: Elementary School Students, Social Emotional Learning, Skill Development, Progress Monitoring
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Sarah R. Powell; Samantha E. Bos; Sarah G. King; Leanne Ketterlin-Geller; Erica S. Lembke – TEACHING Exceptional Children, 2024
Data-based individualization (DBI) is a framework that allows educators to make timely and informed decisions about student progress in academics or behavior. In this article, we focus on the DBI framework as applied to math intervention within a tiered support model for students experiencing math difficulty. We review how DBI starts with an…
Descriptors: Middle School Mathematics, Middle School Students, Middle School Teachers, Mathematics 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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Flanagan, Matthew F.; Kutscher, Elisabeth L. – TEACHING Exceptional Children, 2021
Community-based instruction (CBI) is one type of community experience in which students with disabilities work toward instructional goals while engaged in activities occurring in a natural environment outside of a typical school setting (Hoover, 2016; Rowe et al., 2015). Educators who implement CBI capitalize on their students' time in the…
Descriptors: Community Based Instruction (Disabilities), Progress Monitoring, Students with Disabilities, High School Students
Julie Esparza Brown; Amanda K. Sanford; Donna Sacco – National Center on Intensive Intervention, 2024
This brief highlights how to use culturally and linguistically aligned (CLA) strategies to support multilingual learners within an multi-tiered system of supports (MTSS) framework including how to use a CLA lens to inform instructional adaptations for multilingual learners who require intensive intervention. The brief reviews the use of the CLA…
Descriptors: Multi Tiered Systems of Support, Culturally Relevant Education, Language Usage, Multilingualism
Marx, Teri; Peterson, Amy; Arden, Sarah – National Center on Intensive Intervention, 2020
During spring 2020, educators quickly adapted to providing interventions and collecting data virtually despite the challenges of the COVID-19 pandemic. Parents were critical partners in supporting opportunities for students with intensive needs to data-based individualization (DBI) Process practice and receive feedback and sharing what was working…
Descriptors: COVID-19, Pandemics, Individualized Instruction, Data Use
Juan D’Brot; W. Chris Brandt – Region 5 Comprehensive Center, 2024
In today's educational landscape, state and local educational agencies (SEAs and LEAs) often experience challenges connecting large-scale accountability data with actual school improvement initiatives. These challenges tend to be rooted in incoherent design and use of data systems for continuous improvement. As we aim to support SEAs in…
Descriptors: Educational Improvement, Data Collection, State Departments of Education, School Districts
Data Quality Campaign, 2020
School closures resulting from the COVID-19 pandemic have left educators and families with the responsibility of rapidly learning how to support students while they are at home. This fundamental shift in the way most students receive instruction makes it critical for educators and families to be able to use data to meet students where they are and…
Descriptors: Distance Education, Student Records, COVID-19, Pandemics
Vasquez, Andrea – MDRC, 2020
Millions of students leave college every year before earning a degree. At community colleges, only a third of full-time students graduate within three years. How can school administrators help students stay in school and eventually graduate? Higher education institutions commonly offer students advising services to help them develop the academic…
Descriptors: Community Colleges, Two Year College Students, Program Design, Academic Advising
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Hartong, Sigrid – Critical Studies in Education, 2021
This contribution takes a critical perspective on digital school performance platforms (SPP), which today play a key role in US state education monitoring and accountability. Using examples from two different US state education agencies, I provide an analytical disentanglement of some key dimensions of such platforms' enactment and materiality. I…
Descriptors: State Departments of Education, Technology Uses in Education, Performance, Accountability
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Wilkerson, S. B.; Klute, M.; Peery, B.; Liu, J. – Regional Educational Laboratory Central, 2021
Teachers have access to more data than ever before, including summative (state-level), interim (benchmarklevel), and formative (classroom-level) data. Yet research on how often and why teachers use each type of data is scarce. The Nebraska Department of Education partnered with the Regional Educational Laboratory Central to conduct a study of…
Descriptors: Elementary School Teachers, Secondary School Teachers, Data Use, Teacher Attitudes
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