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Kaitlyn G. Fitzgerald; Elizabeth Tipton – Grantee Submission, 2024
This article presents methods for using extant data to improve the properties of estimators of the standardized mean difference (SMD) effect size. Because samples recruited into education research studies are often more homogeneous than the populations of policy interest, the variation in educational outcomes can be smaller in these samples than…
Descriptors: Data Use, Computation, Effect Size, Meta Analysis
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Denisa Gándara; Hadis Anahideh; Matthew P. Ison; Lorenzo Picchiarini – Grantee Submission, 2024
Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine…
Descriptors: Algorithms, Social Bias, Minority Groups, Equal Education
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Seohyeon Choi; Kristen L. McMaster; Erica S. Lembke; Manjary Guha – Grantee Submission, 2024
Teachers' knowledge and skills about data-based instruction (DBI) can influence their self-efficacy and their implementation of DBI with fidelity, ultimately playing a crucial role in improving student outcomes. The purpose of this brief report is to provide evidence for the technical adequacy of a measure of DBI knowledge and skills in writing by…
Descriptors: Data Use, Writing Instruction, Knowledge Level, Elementary School Teachers
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Tory L. Ash – Grantee Submission, 2024
Within the field of school psychology, we often put a premium on evidence-based practices, but what is often missing from these conversations is a consideration of replication and open science principles in the evaluation of evidence. Amid growing concerns regarding the replicability of psychological science, calls for greater research…
Descriptors: School Psychologists, School Psychology, Open Educational Resources, Replication (Evaluation)
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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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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Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
The gold-standard for evaluating the effect of an educational intervention on student outcomes is running a randomized controlled trial (RCT). However, RCTs may often be small due to logistical considerations, and resulting treatment effect estimates may lack precision. Recent methods improve experimental precision by incorporating information…
Descriptors: Intervention, Outcomes of Education, Randomized Controlled Trials, Data Use