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


Authors : Shadrack Blankson Osei

Volume/Issue : Volume 11 - 2026, Issue 6 - June


Google Scholar : https://tinyurl.com/3tyvtaza

Scribd : https://tinyurl.com/mr2n5srv

DOI : https://doi.org/10.38124/ijisrt/26jun1109

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Abstract : 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.

Keywords : Data-Driven Instruction, Learning Disabilities, Dyslexia, Dyscalculia, Dysgraphia, Response to Intervention, MultiTiered System of Supports, IEP, Curriculum-Based Measurement, Special Education, IDEA, ESSA, Formative Assessment, Progress Monitoring.

References :

  1. American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787
  2. Newman, L., Wagner, M., Knokey, A.-M., Marder, C., Nagle, K., Shaver, D., & Wei, X. (2011). The post-high school outcomes of young adults with disabilities up to 8 years after high school: A report from the National Longitudinal Transition Study-2 (NLTS2) (NCSER 2011-3005). SRI International. https://nlts2.sri.com/reports/2011_09_02/
  3. Butterworth, B., Varma, S., & Laurillard, D. (2011). Dyscalculia: From brain to education. Science, 332(6033), 1049–1053. https://doi.org/10.1126/science.1201536
  4. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  5. Deci, E. L., & Ryan, R. M. (2000). The 'what' and 'why' of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
  6. Berninger, V. W., & Wolf, B. J. (2018). Teaching students with dyslexia, dysgraphia, OWL LD, and dyscalculia (2nd ed.). Paul H. Brookes Publishing.
  7. Fuchs, L. S., & Fuchs, D. (2006). Using CBM for progress monitoring in reading. National Center on Student Progress Monitoring. https://www.studentprogress.org
  8. Fuchs, D., Fuchs, L. S., & Compton, D. L. (2012). Smart RTI: A next-generation approach to multilevel prevention. Exceptional Children, 78(3), 263–279. https://doi.org/10.1177/001440291207800301
  9. Morgan, P. L., Farkas, G., Cook, M., Strassfeld, N. M., Hillemeier, M. M., Pun, W. H., & Schussler, D. L. (2017). Are Black children disproportionately overrepresented in special education? A best-evidence synthesis. Exceptional Children, 83(2), 181–198. https://doi.org/10.1177/0014402916664042
  10. National Center for Education Statistics. (2023). Digest of education statistics: 2022. U.S. Department of Education. https://nces.ed.gov/programs/digest/
  11. Nelson, G., Kiss, A. J., Codding, R. S., McKevett, N. M., Schmitt, J. F., Park, S., Romero, M. E., & Hwang, J. (2023). Review of curriculum-based measurement in mathematics: An update and extension of the literature. Journal of School Psychology, 97, 1–42. https://doi.org/10.1016/j.jsp.2022.12.001
  12. Petscher, Y., Solari, E. J., & Bouchane, M. (2020). Building better treatment outcome models: Consideration of growth and individual differences in reading. Journal of Learning Disabilities, 53(1), 15–27. https://doi.org/10.1177/0022219419895609
  13. Powell, S. R., Lembke, E. S., Ketterlin-Geller, L. R., Petscher, Y., Hwang, J., Bos, S. E., Cox, T., Hirt, S., Mason, E. N., Pruitt-Britton, T., Thomas, E., & Hopkins, S. (2021). Data-based individualization in mathematics to support middle school teachers and their students with mathematics learning difficulty. Studies in Educational Evaluation, 69, 100897. https://doi.org/10.1016/j.stueduc.2020.100897
  14. Regan, K., Evmenova, A. S., Hughes, M. D., Rybicki-Newman, M. P., Gafurov, B., & Mastropieri, M. A. (2021). Technology-mediated writing: It's not how much, but the thought that counts. E-Learning and Digital Media, 18(2), 188–212. https://doi.org/10.1177/2042753021996387
  15. Shaywitz, S. E., & Shaywitz, J. M. (2020). Overcoming dyslexia: A new and complete science-based program for reading problems at any level (2nd ed., completely revised and updated). Alfred A. Knopf.
  16. Snowling, M. J., & Hulme, C. (2021). Annual Research Review: Reading disorders revisited — The critical importance of oral language. Journal of Child Psychology and Psychiatry, 62(5), 635–653. https://doi.org/10.1111/jcpp.13324
  17. Speece, D. L., & Case, L. P. (2001). Classification in context: An alternative approach to identifying early reading disability. Journal of Educational Psychology, 93(4), 735–749. https://doi.org/10.1037/0022-0663.93.4.735
  18. Stecker, P. M., Lembke, E. S., & Foegen, A. (2008). Using progress-monitoring data to improve instructional decision making. Preventing School Failure: Alternative Education for Children and Youth, 52(2), 48–58. https://doi.org/10.3200/PSFL.52.2.48-58
  19. U.S. Department of Education. (2022). 44th annual report to Congress on the implementation of the Individuals with Disabilities Education Act, 2022. Office of Special Education and Rehabilitative Services. https://sites.ed.gov/idea/
  20. U.S. Department of Education, Privacy Technical Assistance Center. (2014). Protecting student privacy while using online educational services: Requirements and best practices. https://studentprivacy.ed.gov/
  21. Wanzek, J., Petscher, Y., Al Otaiba, S., Rivas, B. K., Jones, F. G., Kent, S. C., Schatschneider, C., & Mehta, P. (2017). Effects of a year long supplemental reading intervention for students with reading difficulties in fourth grade. Journal of Educational Psychology, 109(8), 1103–1119. https://doi.org/10.1037/edu0000184
  22. What Works Clearinghouse. (2023). Lexia Core5 reading intervention report. Institute of Education Sciences. https://ies.ed.gov/ncee/wwc/

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.

Keywords : Data-Driven Instruction, Learning Disabilities, Dyslexia, Dyscalculia, Dysgraphia, Response to Intervention, MultiTiered System of Supports, IEP, Curriculum-Based Measurement, Special Education, IDEA, ESSA, Formative Assessment, Progress Monitoring.

Paper Submission Last Date
31 - July - 2026

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