Tuesday, February 11, 2025
Written by
Qian Wang, MEd, PhD,
Hyejeong Yoon

This blog post discusses data-based decision-making and the benefits of using student data to guide instructional decisions in Tier 1 settings. Learn more about related topics, such as data sources, progress monitoring, and fidelity by accessing our supplemental resources at the end of the post.

What Is Data-Based Decision-Making (DBDM)?

Data-based decision-making (DBDM) is one critical component of Multi-Tiered System of Supports, or MTSS. By gathering evidence of student literacy learning, educators can use data to identify students' needs, inform instructional decisions, and monitor the effectiveness of interventions across different tiers of support. DBDM occurs at all levels of MTSS, from the individual student level to the district level. 

Why Does DBDM Matter for Tier 1 Instruction?

Tier 1 instruction, or core instruction, is the foundational level of teaching provided to all students in a classroom. Effectively addressing Tier 1 challenges is critical for effective classwide instruction, ensuring the needs of the majority of students are met through evidence-based practices. When a significant portion of students in a grade are identified as needing Tier 2 or Tier 3 interventions, educators should focus on strengthening core instruction (Burns et al., 2014). One way to do this is through DBDM—also referred to in some studies as data-based instruction (DBI)— which has been shown to improve student literacy outcomes (McMaster et al., 2020). 

Most research studies have focused on effects of DBDM or DBI for delivery of Tier 2 or Tier 3 interventions (e.g., Filderman et al., 2018). However, there is an emerging line of research that shows promising results for engaging in DBDM during core instruction or Tier 1 instruction (Burns et al., 2016; Burns et al., 2024). The specifics of the steps have varied across studies, but there are several common steps that may prove beneficial for educators looking to ensure core instruction supports the vast majority of students. 

What Are the Foundational Elements of DBDM for Core Instruction?

To support Tier 1 instruction, DBDM involves several foundational elements:

  • Regularly collecting student data: DBDM begins with accurate and meaningful gathering of data. This may include data from reliable universal screening and progress monitoring assessments, such as curriculum-based measurements (CBMs). These assessments can provide timely evaluation of the effectiveness of instructional practices (Fry et al., 2024). They can be used to track student performance and determine whether Tier 1 instruction is meeting the needs of all students. Screening helps educators identify which areas (e.g., phonemic awareness, phonics, fluency, comprehension, and vocabulary) require improvement, guiding the initial stages of the decision-making process (Burns et al., 2014).
  • Analyzing student progress toward instructional goals: Analyzing and interpreting collected data is critical for measuring progress, identifying trends, and determining areas that require instructional adjustments. For example, CBM graphs visualize whether students are meeting set instructional goals, allowing teachers to track performance trends and make informed decisions (Van den Bosch et al., 2017).
  • Adjusting instruction with progress monitoring: Make instructional adjustments based on analyzed data to better address student needs. For example, if reading fluency scores reveal challenges, teachers might increase the intensity of reading fluency strategies or use visual aids to clarify concepts. Meanwhile, progress-monitoring tools can be used to track whether implemented adjustments are effective. For example, teachers can compare student performance trendlines to aimlines to determine if adjustments to instruction are producing desired results or if further changes are needed (Filderman et al., 2018; Van den Bosch et al., 2017).
  • Providing professional development and support: Supporting educators through comprehensive professional development (PD), especially PD focused on DBDM, is essential. It has been found that many educators lack the knowledge and skills needed to engage in instructional decision-making using data, even when they regularly collect CBM data (Fry et al., 2024). Educators may struggle to analyze data and translate it into instructional practices. By participating in training sessions, teachers' knowledge and skills can be improved regarding instructional decision-making in response to data (Gesel et al., 2021). In addition, engaging in professional learning communities (PLCs) or team discussions can empower teachers to review data and refine strategies collectively. This collaboration can support consistent instruction across classrooms (Filderman et al., 2018; Kearns et al., 2021).

What Data Can Be Used for Instructional DBDM?

There is a wide range of data sources that educators and school administrators can use for instructional decision-making. These data sources can reflect not only student academic and behavioral data but also teacher knowledge and skills.  

  • Student Academic and Behavioral Data
  • Curriculum-based measurements (CBMs): student scores from universal screening, diagnostic assessments, standardized tests, and other low-stakes assessments used to track day-to-day learning progress
  • Classroom and school behavior: observational data collected during classroom activities or structured behavior tracking 
  • District/state assessments: data from large-scale comparative studies (e.g., National Assessment of Educational Progress [NAEP]) or high-stake assessments (e.g., Iowa Statewide Assessment of Student Progress [ISASP])
  • Teacher Knowledge and Skill
  • Instructional practices: observing use of evidence-based practices, such as modeling and guided practice to teach early literacy skills, during instruction 
  • Fidelity data: data on fidelity of implementation for instruction and intervention 
  • Professional development outcomes: data on teacher growth in DBDM knowledge and skills following training sessions

By collecting and synthesizing these diverse data sources, educators and administrators can build a holistic understanding of students’ progress toward instructional goals and make informed instructional decisions that support both students and educators.

Supplemental Resources

References

Blumenthal, S., Blumenthal, Y., Lembke, E. S., Powell, S. R., Schultze-Petzold, P., & Thomas, E. R. (2021). Educator perspectives on data-based decision making in Germany and the United States. Journal of Learning Disabilities, 54(4), 284–299. https://doi.org/10.1177/0022219420986120 

Burns, M. K., Karich, A. C., Maki, K. E., Anderson, A., Pulles, S. M., Ittner, A., & Helman, L. (2014). Identifying classwide problems in reading with screening data. Journal of Evidence-Based Practices for Schools, 14(2), 186–204.

Burns, M. K., Pulles, S. M., Helman, L., McComas, J., Blake, J. J., & Graves, S. L. (2016). Assessment-based intervention frameworks: An example of a Tier 1 reading intervention in an urban school. In Psychoeducational assessment and intervention for ethnic minority children: Evidence-based approaches (pp. 165–182). American Psychological Association. https://doi.org/10.1037/14855-010 

Burns, M. K., Duesenberg-Marshall, M. D., & Romero, M. E. (2024). Effects of a classwide reading intervention on reading fluency and comprehension of content area text with students in middle school. The Journal of Educational Research, 117(6), 378– 386. https://doi.org/10.1080/00220671.2024.2423185

Filderman, M. J., Toste, J. R., Didion, L. A., Peng, P., & Clemens, N. H. (2018). Data-based decision making in reading interventions: A synthesis and meta-analysis of the effects for struggling readers. The Journal of Special Education, 52(3), 174–187. https://doi.org/10.1177/0022466918790001 

Fry, E. C., Toste, J. R., Feuer, B. R., & Espin, C. A. (2024). A systematic review of CBM content in practitioner-focused journals: Do we talk about instructional decision-making? Journal of Learning Disabilities, 57(5), 275–290. https://doi.org/10.1177/00222194231215031 

Gesel, S. A., LeJeune, L. M., Chow, J. C., Sinclair, A. C., & Lemons, C. J. (2021). A meta-analysis of the impact of professional development on teachers’ knowledge, skill, and self-efficacy in data-based decision-making. Journal of Learning Disabilities, 54(4), 269–283. https://doi.org/10.1177/0022219420970196 

Kearns, D. M., Feinberg, N. J., & Anderson, L. J. (2021). Implementation of data-based decision-making: Linking research from the special series to practice. Journal of Learning Disabilities, 54(5), 365–372. https://doi.org/10.1177/00222194211032403 

McMaster, K. L., Lembke, E. S., Shin, J., Poch, A. L., Smith, R. A., Jung, P.-G., Allen, A. A., Wagner, K., Kendeou, P., & Graham, S. (2020). Supporting teachers’ use of data-based instruction to improve students’ early writing skills. Journal of Educational Psychology, 112(1), 1–21. https://doi.org/10.1037/edu0000358 

van den Bosch, R. M., Espin, C. A., Chung, S., & Saab, N. (2017). Data–based decision–making: Teachers’ comprehension of curriculum-based measurement progress-monitoring graphs. Learning Disabilities Research & Practice, 32(1), 46–60. https://doi.org/10.1111/ldrp.12122 

Wilcox, G., Fernandez Conde, C., & Kowbel, A. (2021). Using evidence-based practice and data-based decision making in inclusive education. Education Sciences, 11(129). https://doi.org/10.3390/educsci11030129