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REL Central Ask A REL Response

Data Use

December 2019


How effective is data-based instruction for students with disabilities? What factors contribute to it being effective?


Following an established REL Central research protocol, we conducted a search for research reports as well as descriptive study articles to help answer the question. The resources included ERIC and other federally funded databases and organizations, research institutions, academic databases, and general Internet search engines. (For details, please see the methods section.)

We have not evaluated the quality of references and the resources provided in this response, and we offer them only for your reference. Also, we compiled the references from the most commonly used resources of research, but they are not comprehensive and other relevant references and resources may exist.

Research References

Ernest, J. M., Heckaman, K. A., Thompson, S. E., Hull, K. M., & Carter, S. W. (2011). Increasing the teaching efficacy of a beginning special education teacher using differentiated instruction: A case study. International Journal of Special Education, 26(1), 191–201. Retrieved from

From the abstract:

“This article provides a description of how a beginning special education teacher in an inclusion classroom used pre-assessment, self-assessment, and on-going assessment to implement the principles of differentiated instruction to become more responsive to her students’ needs in a systematic way. This article describes a case study of one beginning teacher’s use of differentiated instruction. First, a discussion of the usefulness of differentiated instruction in increasing the likelihood of success for children with disabilities is provided. Next, qualitative data supported the implementation of the differentiated instruction process to help the teacher realize how she could positively impact students’ learning using Tomlinson’s (2000) categories of content, product, process, and learning environments. Finally, recommendations are provided for how to engage teachers to implement differentiated instruction as a data-based iterative process of using evidence-based practices to meet the needs of all children in an inclusion classroom.”

Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009–4067). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. Retrieved from

From the introduction:

“As educators face increasing pressure from federal, state, and local accountability policies to improve student achievement, the use of data has become more central to how many educators evaluate their practices and monitor students’ academic progress. Despite this trend, questions about how educators should use data to make instructional decisions remain mostly unanswered. In response, this guide provides a framework for using student achievement data to support instructional decision making. These decisions include, but are not limited to, how to adapt lessons or assignments in response to students’ needs, alter classroom goals or objectives, or modify student-grouping arrangements. The guide also provides recommendations for creating the organizational and technological conditions that foster effective data use. Each recommendation describes action steps for implementation, as well as suggestions for addressing obstacles that may impede progress. In adopting this framework, educators will be best served by implementing the recommendations in this guide together rather than individually.”

Jung, P.-G., McMaster, K. L., & delMas, R. C. (2016). Effects of early writing intervention delivered within a data-based instruction framework. Exceptional Children, 83(3), 281–297. Retrieved from

From the abstract:

“We examined effects of research-based early writing intervention delivered within a data-based instruction (DBI) framework for children with intensive needs. We randomly assigned 46 students with and without disabilities in Grades 1 to 3 within classrooms to either treatment or control. Treatment students received research-based early writing intervention within a DBI framework for 30 min, 3 times per week, for 12 weeks. Control students received business-as-usual writing instruction. We measured writing performance using curriculum-based measures (CBM) and Woodcock Johnson III Tests of Achievement (WJ III). We found significant treatment effects on CBM outcomes (Hedges g = 0.74 to 1.36). We also found a significant interaction between special education status and condition on the WJ III favoring treatment students with disabilities (Hedges g = 0.45 to 0.70). Findings provide preliminary support for using a combination of research-based intervention and DBI with students with intensive writing needs.”

Lembke, E. S., McMaster, K. L., Smith, R. A., Allen, A., Brandes, D., & Wagner, K. (2017). Professional development for data-based instruction in early writing: Tools, learning, and collaborative support. Teacher Education and Special Education, 41(2), 106–120. Retrieved from

From the abstract:

“Few teachers receive adequate preparation to provide effective individualized instruction for children with intensive early writing needs. In this article, the authors describe an attempt to close this learning gap, by developing Data-Based Instruction-Tools, Learning, and Collaborative Support (DBI-TLC), a comprehensive professional development (PD) system that provides tools, learning opportunities, and ongoing collaborative supports for teachers to implement DBI in early writing. They describe the theoretical framework that has guided this work, the teacher population with whom they worked, their approach to assessing important teacher outcomes, and their development process. They highlight key findings that align with their theory of change, and discuss implications for further research and teacher preparation.”

Lemons, C. J., Kearns, D. M., & Davidson, K. A. (2014). Data-based individualization in reading: Intensifying interventions for students with significant reading disabilities. TEACHING Exceptional Children, 46(4), 20–29. Retrieved from
Full text available at

From the abstract:

“It is a few weeks before the end of the school year. Mrs. Arnold, a special education teacher at Cornelius Elementary, is analyzing the most recent progress-monitoring data for one of her third grade students, Rashan. As she looks at the data she has just plotted on a graph, Mrs. Arnold pauses to reflect. Despite her efforts to increase Rashan’s reading skills, the improvements he has made across the school year are inadequate. Mrs. Arnold remembers a presentation that she attended at the annual CEC conference earlier in the year and accesses the website she had written in her notes– She begins to explore the website and wonders whether the recommended approach, data-based individualization (DBI), could help her increase Rashan’s reading performance.”

Lemons, C. J., Sinclair, A. C., Gesel, S., Gruner Gandhi, A., & Danielson, L. (2017). Supporting implementation of data-based individualization: Lessons learned from NCII’s first five years. Washington, DC: U.S. Department of Education, Office of Special Education Programs, National Center on Intensive Intervention at American Institutes for Research. Retrieved from

From the ERIC abstract:

“The Office of Special Education Programs (OSEP) in the U.S. Department of Education funded the National Center on Intensive Intervention (NCII, or the Center) in 2011. NCII’s mission during the first five years of funding was to build the capacity of local education agencies (LEAs) to support schools, practitioners, and other stakeholders in the implementation of intensive intervention in reading, mathematics, and behavior for students with severe and persistent learning and/or behavioral needs. The purpose of this document is to provide an overview of the Center’s accomplishments during the initial funding cycle and to highlight a set of lessons learned from the 26 schools that implemented intensive intervention while receiving technical support from the Center. First, the authors provide a description of NCII’s approach to intensive intervention and summarize the Center’s initial technical assistance efforts. Next, they describe their methodology and outline the lessons learned from the implementation sites. Then, they offer guidance for practitioners who are interested in implementing intensive intervention. Finally, the authors close with an overview of NCII’s plans for building upon this work over the next five years.”

Powers, K., & Mandal, A. (2011). Tier III assessments, data-based decision making, and interventions. Contemporary School Psychology, 15, 21–33. Retrieved from

From the abstract:

“Within the Response-to-Intervention framework, students who fail to profit from high-quality general education instruction, accommodations, and supplemental instruction progress to a more intensive intervention program, sometimes referred to as ‘Tier III.’ This article describes a problem-solving approach to designing such intensive, data-based, and scientifically supported interventions for students with pervasive reading problems who have failed to respond to less rigorous services. The application of well-established (i.e., progress monitoring) and emerging methods (i.e., brief experimental analysis) for optimizing interventions are described. Two case studies are provided to illustrate how these techniques may be combined to implement Tier III interventions.”

Reed, D. K. (2015). Middle level teachers’ perceptions of interim reading assessments: An exploratory study of data-based decision making. Research in Middle Level Education Online, 38(6), 1–13. Retrieved from

From the abstract:

“This study explored the data-based decision making of 12 teachers in grades 6–8 who were asked about their perceptions and use of three required interim measures of reading performance: oral reading fluency (ORF), retell, and a benchmark comprised of released state test items. Focus group participants reported they did not believe the benchmark or ORF tests accurately reflected students’ comprehension abilities. Teachers held more favorable opinions of retell but admitted improvising their use of the measure rather than following mandated implementation procedures. Participants reported that only summative state assessment scores were used to plan appropriate instruction and only for large groups. Results suggest the need for improved support for data-based decision making and the development of technically adequate interim measures with relevance to the teachers expected to use them.”

Solís, M., El Zein, F., Black, M., Miller, A., Therrien, W. J., & Invernizzi, M. (2018). Word study intervention for students with ASD: A multiple baseline study of data-based individualization. Education and Training in Autism and Developmental Disabilities, 53(3), 287–298. Retrieved from

From the abstract:

“This multiple baseline across participants study examined the efficacy of a data-based individualization word study intervention for students with autism spectrum disorder (N = 5) and low word reading skills. An experienced interventionist provided 1:1 word reading instruction in 30-minute sessions five times per week for an average of 10 sessions per participant. Intervention effects for directly taught words and words with similar spelling patterns were estimated using visual analysis and calculation of mean differences across baseline and intervention phases. Results indicate immediate and consistent improvements in word reading outcomes across all participants.”

Svinicki, M. D., Williams, K., Rackley, K., Sanders, A. J. Z., Pine, L., & Stewart, J. (2016). Factors associated with faculty use of student data for instructional improvement. International Journal for the Scholarship of Teaching and Learning, 10(2), 1–8. Retrieved from

From the abstract:

“Much is being said in education about the value of adopting data-based or analytics approaches to instructional improvement. One important group of stakeholders in this effort is the faculty. ‘In many cases, the key constituency group is faculty, whose powerful voice and genuine participation often determine the success or failure of educational innovations, especially those that involve pedagogical and academic change’ (Furco & Moely, 2012, pg. 129). This paper reports the results of an exploration of factors that influence faculty to consider or reject using analysis of student data to improve instruction based on social cognitive theory. Self-efficacy, value of the outcome, and feasibility of using a student data-based reflection process were found to be related to the actual use of components of the reflection process by faculty.”

Additional Organizations to Consult

National Center on Intensive Intervention (NCII) at American Institutes for Research:

From the website:

“NCII builds the capacity of state and local education agencies, universities, practitioners, and other stakeholders to support implementation of intensive intervention in literacy, mathematics, and behavior for students with severe and persistent learning and/or behavioral needs, often in the context of their multi-tiered system of support (MTSS) or special education services. NCII’s approach to intensive intervention is data-based individualization (DBI), a research-based process that integrates the systematic use of assessment data, validated interventions, and intensification strategies.”


Search Strings

The following keywords and search strings were used to search the reference databases and other sources:

  • Data-based instruction
  • Data-based intervention

Databases and Resources

REL Central searched ERIC for relevant references. ERIC is a free online library, sponsored by the Institute of Education Sciences, of over 1.6 million citations of education research. Additionally, we searched Google Scholar.

Reference Search and Selection Criteria

When searching and reviewing resources, we considered the following criteria:

  • Date of the Publication: The search and review included references published between 2009 and 2019.
  • Search Priorities of Reference Sources: Search priority was given to ERIC, followed by Google Scholar.
  • Methodology: The following methodological priorities/considerations were used in the review and selection of the references: (a) study types, such as randomized controlled trials, quasi experiments, surveys, descriptive analyses, literature reviews; and (b) target population and sample.

This memorandum is one in a series of quick-turnaround responses to specific questions posed by educational stakeholders in the Central Region (Colorado, Kansas, Missouri, Nebraska, North Dakota, South Dakota, Wyoming), which is served by the Regional Educational Laboratory Central at Marzano Research. This memorandum was prepared by REL Central under a contract with the U.S. Department of Education’s Institute of Education Sciences (IES), Contract ED-IES-17-C-0005, administered by Marzano Research. Its content does not necessarily reflect the views or policies of IES or the U.S. Department of Education nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.