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Apryl L. Poch; Pyung-Gang Jung; Kristen L. McMaster; Erica S. Lembke – Grantee Submission, 2025
Data-Based Instruction (DBI) has a strong empirical base for supporting the intensive academic needs of students who do not respond to standard treatment protocols. However, teachers use DBI infrequently in practice. In a previous study (Poch et al., 2020), teachers reported supports such as coaching facilitated DBI implementation, whereas access…
Descriptors: Data Use, Teaching Methods, Faculty Development, Special Education Teachers
Ginger Elliott-Teague – Center for IDEA Early Childhood Data Systems (DaSy), 2025
Linking and integrating data from different data systems can help U.S. states, territories, and other entities answer critical questions about children and families, their needs, and experiences with IDEA services and supports. The 2021 State of the States Survey provides information about how Part C early intervention (EI) programs and Part B 619…
Descriptors: Early Childhood Education, Early Intervention, Equal Education, Federal Legislation
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Lisa Didion; Lauren Bruno; Gianna Marshall; Jordan Immerfall; Amanda Kunkel; Jennifer McGinn – Career Development and Transition for Exceptional Individuals, 2025
Intervention studies focused on improving reading outcomes are scarce for postsecondary students with disabilities. Historically, comprehensive transition and postsecondary education programs (CTPs) for students with disabilities focused on employment, living, personal, and social skills over academic instruction. Recently, there is an emphasis to…
Descriptors: Postsecondary Education, College Students, Students with Disabilities, Data Use
Ginger Elliott-Teague; Shilan Wooten – Center for IDEA Early Childhood Data Systems (DaSy), 2025
High-quality state early intervention (IDEA Part C) data systems enable state staff to use data to improve their programs and results for children and families. The 2021 State of the States Survey data indicate that most early intervention (EI) programs had state data systems with essential child-level data elements, including child outcomes. In…
Descriptors: Equal Education, Educational Legislation, Federal Legislation, Students with Disabilities
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Meagan Karvonen; Lindsay Ruhter; Amy K. Clark – Exceptionality, 2024
Students with extensive support needs (ESN), most of whom are taught in separate settings by special educators without extensive academic preparation, have difficulty making progress in the general education curriculum. Although there is evidence that data-based decision-making improves achievement for a wide range of students, there is little…
Descriptors: Students with Disabilities, Academic Education, Data Use, Decision Making
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Toyokawa, Yuko; Horikoshi, Izumi; Majumdar, Rwitajit; Ogata, Hiroaki – Smart Learning Environments, 2023
In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring…
Descriptors: Barriers, Affordances, Artificial Intelligence, Inclusion
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Danielle Mireles; Claudia Chiang-Lopez – Journal of Postsecondary Education and Disability, 2025
This article examines how carceral logics manifest for undergraduate racialized and disabled students who identify as or have a lived experience of disability. Using Disability Critical Race Theory, a crip-of-color critique, and carceral ableism and sanism as lenses, we challenge color-evasive ideology and explore how services that purport to…
Descriptors: Undergraduate Students, Students with Disabilities, Critical Race Theory, Attitudes toward Disabilities
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Matthew T. Marino; Eleazar Vasquez III – Journal of Special Education Leadership, 2024
This manuscript presents an exploratory mixed-methods case study examining the impact of artificial intelligence (AI) in the form of generative pretrained transformers (GPTs) and large language models on special education administrative practices in one school district in the Northeast United States. AI holds tremendous potential to positively…
Descriptors: Special Education, Administrators, Artificial Intelligence, Data Use
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Julie Irene Bost; Carl Lashley – Journal of Special Education Leadership, 2025
The purpose of this interview-based qualitative study was to explore how individualized education program (IEP) team members determine least restrictive environment and educational placement. The Individuals with Disabilities Education Act (IDEA) requires students with disabilities to be educated in the least restrictive environment and to the…
Descriptors: Mainstreaming, Individualized Education Programs, Students with Disabilities, Student Placement
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Müller, Eve; Wood, Caitlin; Cannon, Lynn; Childress, Deb – Focus on Autism and Other Developmental Disabilities, 2023
This pilot study examined (a) the perceived barriers to creating high-quality social and emotional learning (SEL) IEP goals for autistic students without intellectual disabilities, and (b) the impact of using a data-driven SEL IEP goal builder--a key component of the Ivymount Social Cognition Instructional Package (IvySCIP)--on the quality of SEL…
Descriptors: Autism Spectrum Disorders, Students with Disabilities, Barriers, Social Emotional Learning
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Belmonte-Mulhall, Colleen P.; Harrison, Judith R. – Journal of Applied School Psychology, 2023
Students with or at-risk of High Incidence Disabilities (HID) experience negative short and long-term outcomes. To intervene, many schools have elected to implement evidence-based practices within Multi-Tiered Systems of Support (MTSS), such as Response to Intervention (RTI). MTSS target the academic and behavioral progress of students deemed 'at…
Descriptors: Multi Tiered Systems of Support, Students with Disabilities, Student Behavior, Data Interpretation
Betsy Wolf – Grantee Submission, 2024
The What Works Clearinghouse (WWC) at the Institute of Education Sciences reviews rigorous research on educational practices, policies, programs, and products with a goal of identifying 'what works' and making that information accessible to the public. One critique of the WWC is the need to more closely examine 'what works' for whom, in which…
Descriptors: Data Use, Educational Research, Student Characteristics, Context Effect
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Brown, Kirsten R.; Wilke, Autumn K.; Pena, Maria – Journal of Postsecondary Education and Disability, 2020
Caseload (student-to-staff ratio) is a metric commonly used by upper level administrators to inform budgetary allocations. Using a national, random sample we found that the average caseload is 133.0 students per disability practitioner. Institutions with one disability practitioner had a caseload of 154.9 students; institutions with two or three…
Descriptors: Budgeting, Resource Allocation, Students with Disabilities, Caseworkers
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Emma Shanahan; Seohyeon Choi; Jechun An; Bess Casey-Wilke; Seyma Birinci; Caroline Roberts; Emily Reno – Grantee Submission, 2025
Although data-based individualization (DBI) has positive effects on learning outcomes for students with learning difficulties, this framework can be difficult for teachers to implement due to its complexity and contextual barriers. The first aim of this synthesis was to investigate the effects of ongoing professional development (PD) support for…
Descriptors: Data Use, Individualized Instruction, Learning Problems, Students with Disabilities
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Emma Shanahan; Seohyeon Choi; Jechun An; Bess Casey-Wilke; Seyma Birinci; Caroline Roberts; Emily Reno – Journal of Learning Disabilities, 2025
Although data-based individualization (DBI) has positive effects on learning outcomes for students with learning difficulties, this framework can be difficult for teachers to implement due to its complexity and contextual barriers. The first aim of this synthesis was to investigate the effects of ongoing professional development (PD) support for…
Descriptors: Data Use, Individualized Instruction, Learning Problems, Students with Disabilities
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