Data-Based Decision-Making (DBDM) Teams in Middle School: What Progress Have We Made in Middle School DBDM in the Past 10 Years?

During my time as editor of Assessment for Effective Intervention (AEI), I was so pleased to help advance scholarship at the point where assessment and intervention meet and are supported by teacher data-based decision-making (DBDM). My training at the University of Minnesota with Stan Deno and Chris Espin had helped me foster my own work in developing Curriculum-Based Measures (CBMs) and then supporting teachers as they collected progress monitoring data in academic areas and utilized these data to make more effective decisions about student instruction. So AEI was a natural fit for my interests! I was so fortunate to be supported by amazing colleagues including Sarah Conoyer, the editorial assistant for the journal, and associate editors who were leaders in their own fields of study, including Devin Kearns and Jessica Toste in literacy, Yaacov Petscher in measurement, Sarah Powell and Elizabeth Hughes in mathematics, and Steve Kilgus and Tyler Renshaw in behavior, among others. One of the key goals as editor was to move toward attaining an impact factor for the journal, so we continued to work on timely, specific, and constructive feedback to authors, involving international authors in submission and review, and considering ways to get more readership (and thus, hopefully more citation) of articles from the journal. We continued to have a student editorial board, developed by my predecessors, and at the time, AEI was the only journal with a student editorial board—a great opportunity to get students involved in the peer review process! We also celebrated an article of the year and reviewer of the year, which was a great way to acknowledge effort in a more public way. During my time as editor, I read more research that I ever had and got really interested in more technical methods for evaluating assessments. It was such a rich learning experience for me, and I’m so happy to have been provided the opportunity! Because of my interest in DBDM, it is likely not surprising that I chose an article focused on this area to highlight for this special issue celebrating AEI’s anniversary.

As we consider evidence-based frameworks for supporting students who have learning challenges in schools, Data-Based Decision-Making (Deno & Mirkin, 1977; Harlacher et al., 2024) continues to be a solid, supported practice (Jung et al., 2018). DBDM, originally conceptualized by Stan Deno and his colleagues at the University of Minnesota, is a model where high-quality instruction and intervention, delivered with fidelity, is considered in light of frequently collected and graphed student data. Teachers utilize decision-making guidelines or a rubric to examine graphed data and make decisions about whether to proceed or make modifications to the current instructional platform. In this way, teachers are responding to data in a timely manner, suggesting appropriate changes that might benefit student academic outcomes (McMaster et al., 2025). There is a great deal of research to support the use of DBDM and related concepts like Data-Based Individualization (DBI; Shanahan et al., 2025), but primarily at the elementary level (i.e., Lembke et al., 2024). Arguably, there is just as much, or perhaps even greater need, for DBDM at the middle and high school levels. Students who have a history of difficulty in academic areas have compounded needs as they move into middle and high school. National data suggest that students with disabilities continue to achieve at a basic or below basic level at eighth grade in reading and mathematics (U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress [NAEP], 2024). This lack of achievement is compounded with difficulties in areas like executive functioning, motivation, and study skills. Together, these areas of need combine to create specific and unique profiles for secondary students who are experiencing difficulty or have disabilities. Thus, there is a strong need for teams to come together on a frequent basis to utilize student data to make decisions about how instruction and intervention are functioning for each student.

What might DBDM look like at the middle school level? This is precisely what Crone and colleagues were attempting to understand in their 2016 study. In this study, the research team examined the DBDM team meetings at 25 middle schools, hoping to find out information that aligns with two research questions: (a) How is DBDM implemented in the middle schools in the sample? They describe the structure, process, content, and decisions made in the observed data team meetings. (b) To what extent do self-reported data team practices align with observed data team practices? The authors examine the alignment between responses on a standardized survey of school practices with the findings from multiple, standardized observations of data team meetings at each participating school.

For their comprehensive study, they observed middle school teams at 25 middle schools 3 times during the year (fall, winter, and spring). In terms of results, the team found that the time teams were spending was not always utilized efficiently and productively, with just 2.5 n per individual discussion, on average. With respect to data sources, high-stakes test scores were often referenced in meetings where reading was the primary issue, while quantitative data were utilized only 6% of the time on average in meetings where behavior was the primary issue. A lack of actionable decisions and follow-up on those decisions was the final area noted in the results. Crone and colleagues did provide suggestions for next steps, with a primary one focused on a post-team meeting self-check to determine whether the team has met key objectives, including the following questions:

(a) What decisions have we made that we expect to lead to improved student performance? (b) Did we have all of the relevant data we needed to make the decisions? (c) Is the process for implementing the decisions clear (e.g., person assigned, goal set, timeline established), and (d) To what extent did we address the needs of the students that we designated as highest priority? (p. 90)

So how does this relate to our current knowledge in the area of middle school DBDM? While DBDM has made advances at the elementary level in terms of resources and knowledge of assessments, interventions, and decision-making frameworks, the literature is still scant at the middle school level. In my own work with colleagues utilizing a DBI framework for middle school mathematics, our team provides professional learning and coaching to teachers in the areas of progress monitoring and diagnostic assessment, as well as evidence-based mathematics strategies and decision-making (Powell et al., 2021). Based on our data so far, the most difficult piece is getting teachers to consistently monitor the progress of students and then utilize the data. This is similar to what Crone and her colleagues found as well.

What are the next steps in middle school DBDM implementation? One key seems to be utilizing tools and a system that fits into what teachers are already doing—not adding on top of their current load. In addition, connecting to school or state standards seems to be a piece that is particularly important at the middle school level. Finally, given that middle school teachers typically have larger class sizes, streamlining the DBDM materials and process in any way possible is encouraged. For instance, we have tried multiple progress monitoring systems to find the one that is the best fit for our buildings.

The use of data-based decision-making teams to support students with academic or behavioral difficulties remains as relevant in 2025 as it was in 2016. We need to capitalize on this past work and make sure we are building on existing research as we move forward, and this existing research should include work by international scholars such as Kim Schildkamp, Stefan Vos, and Natalie Forrester. Perhaps most importantly, we need to talk to teachers to see what might work best, rather than imposing upon them a system that is not a good fit.