Data-Driven Instruction as a Framework for Improving Learning Outcomes Among Students with Learning Disabilities in U.S. K–12 Schools

Students with learning disabilities make up about 14% of the total number of students receiving special education services under the Individuals with Disabilities Education Act (IDEA), but their continued underachievement compared to their general education peers suggests that traditional teaching methods are not effective in addressing their individual needs. The three most common high-incidence learning disabilities in American schools – dyslexia, dysgraphia and dyscalculia – suffer from a lack of responsiveness in one-size-fits-all curriculum designs that focus exclusively on speed. This paper analyzes a systematic literature review of peer-reviewed empirical studies and federal education reports published from 2015 to 2025, which examines the effectiveness of data-driven instruction (DDI) as a practice implemented within two frameworks: the Response to Intervention (RTI) and Multi-Tiered System of Supports (MTSS). Results across all studies are highly consistent and show moderate but consistent improvements in reading, written expression, and mathematics, with effect sizes typically in the range of g = 0.30 to g = 0.50 across well-controlled studies, for students with LD who participated in schools with structured, data-driven cycles of instruction that included curriculum-based measurement, adaptive technology, and IEP goal alignment. In addition to academic achievement, DDI helps identify learning gaps early, boosts student involvement and aids teacher decision-making responsiveness. Implications include pre-service teacher preparation, district-wide MTSS infrastructure, and federal policy alignment with IDEA and Every Student Succeeds Act (ESSA). The purpose of this review is to assert that data-driven instruction is not only a pedagogical choice, but a moral and legal obligation in U.S. schools for equitable and effective special education.