JOURNAL ARTICLE
Measuring Elementary Student Reading Gain in the Context of School-Implemented Multitiered Systems of Support.
Published In: Remedial & Special Education, 2026, v. 47, n. 1. P. 53 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Al Otaiba, Stephanie; van Dijk, Wilhelmina; Stewart, Jennifer; Edwards, Ashley A.; Russell Freudenthal, Dayna; Allor, Jill; Schatschneider, Christopher; Yovanoff, Paul 3 of 3
Abstract
This article examines student reading gains across Grades 1 to 5 within typical school-implemented Response to Intervention (RTI) frameworks, using Measures of Academic Progress (MAP) computer-adaptive test data from 10 elementary schools. Findings indicate that first graders showed greater reading growth than students in higher grades, and students without disabilities or 504 plans made larger gains than those with high- or low-incidence disabilities or 504 plans. Students receiving supplemental Tier 2 or Tier 3 interventions did not catch up to peers in Tier 1, but greater reading gains among Tier 2/3 students were associated with schools where administrators more clearly articulated RTI components such as progress monitoring, data-based decision-making, and Tier 2 instruction. The study highlights the potential role of administrator leadership in effective RTI implementation and underscores the need for early, intensive, and tailored interventions, while noting limitations including sample size, reliance on administrator self-report, and correlational design.
Additional Information
- Source:Remedial & Special Education. 2026/02, Vol. 47, Issue 1, p53
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2026
- ISSN:0741-9325
- DOI:10.1177/07419325241268563
- Accession Number:190817741
- Copyright Statement:Copyright of Remedial & Special Education is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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