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Ashley Sanabria; Jin Kyoung Hwang; Elham Zargar; Deborah Lowe Vandell; Carol M. Connor – Grantee Submission, 2024
Purpose: Having a clear understanding of the types and amounts of classroom literacy learning opportunities is important for contextualizing children's early literacy performance. In this study, we examine the content, context, and management of literacy learning opportunities in a large, geographically diverse sample in the United States. We also…
Descriptors: Preschool Education, Primary Education, Emergent Literacy, Oral Language
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Yasemin Copur-Gencturk; Jingxian Li; Allan S. Cohen; Chandra Hawley Orrill – Grantee Submission, 2024
Scholars and practitioners have called for personalized and widely accessible professional development (PD) for teachers. Yet, a long-standing tension between customizing support and increasing access to such support has hindered the scale-up of high-quality PD for individual teachers. This study addresses this challenge by developing a…
Descriptors: Professional Development, Electronic Learning, Individualized Instruction, Program Effectiveness
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Cindy Peng; Conrad Borchers; Vincent Aleven – Grantee Submission, 2024
Prior studies identified effective, but mainly non-digital, homework aids. This research involved 18 middle school students in a lo-fi prototyping study to integrate traditional homework support tools with intelligent tutoring systems (ITS), leveraging rich log data for personalized learning. Feature investigations in standardized diaries, goal…
Descriptors: Middle School Students, Intelligent Tutoring Systems, Homework, Design
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Duy M. Pham; Kirk P. Vanacore; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Effective personalization of education requires knowing how each student will perform under certain conditions, given their specific characteristics. Thus, the demand for interpretable and precise estimation of heterogeneous treatment effects is ever-present. This paper outlines a new approach to this problem based on the Leave-One-Out Potential…
Descriptors: Middle School Students, Middle School Teachers, Middle School Mathematics, Algebra
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Seohyeon Choi; Kristen L. McMaster; Nidhi Kohli; Emma Shanahan; Seyma Birinci; Jechun An; McKinzie Duesenberg-Marshall; Erica S. Lembke – Grantee Submission, 2024
For students with intensive learning needs for whom standard, validated interventions do not effectively promote academic growth, data-based instruction (DBI) is suggested as an effective, fine-grained approach to individualization. Key to DBI's success is making instructional changes based on individual students' progress monitoring data. The…
Descriptors: Individualized Instruction, Writing Difficulties, Special Needs Students, Elementary School Students
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Sharon Vaughn; Jeanne Wanzek; Leticia R. Martinez; Eleanor M. Hancock; Anna-Mari Fall; S. Blair Payne; Sally K. Fluhler – Grantee Submission, 2025
This pilot study investigated the efficacy of the Promoting Adolescent Comprehension Through Text (PACT) intervention, a social studies content knowledge and reading comprehension set of practices implemented with social studies classes including students with disabilities. Social studies general education teachers were provided with professional…
Descriptors: Pilot Projects, Content Area Reading, Faculty Development, Social Studies
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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