To determine whether Tier 1.5 is needed, educators must gather student data, identify appropriate cut scores, and determine a recommendation for next steps.

Is Tier 1.5 Needed? Steps to Consider for a Classwide Reading Intervention

Tuesday, July 22, 2025

Written by

Qian Wang, MEd, PhD,

Shawn Datchuk, MEd, PhD

Prior research suggests classwide reading interventions can supplement and strengthen core instruction (e.g., Maki et al., 2022). Some researchers conceptualize classwide interventions as part of Multi-Tiered System of Supports (MTSS) as Tier 1.5 (Kovaleski et al., 2023). In a prior blog post, we contextualized Tier 1.5 within legislative- and research-based definitions of MTSS. In the current post, we provide guidance on how to determine if a Tier 1.5 classwide intervention is necessary.

An important aspect of MTSS is data-based decision-making (DBDM), which involves use of reliable and valid assessments to determine if additional tiers of support are needed, such as Tier 2 or Tier 3 (e.g., Fien et al., 2021). Traditionally, DBDM is used to identify individual students for Tiers 2 or 3. Conversely, in Tier 1.5, DBDM is used to identify entire classrooms (e.g., 20 students in a first-grade classroom) for intervention. Because of this important difference, we provide some factors to consider when engaging in DBDM for Tier 1.5.

Types of Assessments

It is beneficial to consider multiple types of assessments and data to determine if Tier 1.5 is necessary. Relying on a single assessment data point may lead to inaccurate decisions regarding the need for additional support. As noted in a systematic review of Tier 2 intervention (Truckenmiller & Brehmer, 2021), two or more types of assessments are commonly used when determining the need for additional tiers of support.

Broadly speaking, there are two types of assessments to consider (Hosp et al., 2016): curriculum-based assessment (CBA) and curriculum-based measurement (CBM) tasks. CBA tasks directly align to skills taught through daily instruction. Common CBA tasks include teacher-created tests (e.g., quizzes, exit tickets, or daily assessments) and assessments found within classroom curricula (e.g., end-of-lesson tests or chapter tests). Because CBA tasks directly align with instruction, they can provide precise information on specific areas of need; however, CBAs may not capture data on all critical content and may not be a reliable or valid indicator of overall performance across the school year (Datchuk et al., 2024). For example, student performance may fluctuate across CBA tasks (e.g., low scores on Monday but high scores on Wednesday), or scores may not be related to other established measures of reading, such as performance on statewide reading assessments.

In contrast to CBA tasks, CBM tasks cover a broad survey of all skills to be learned by the end of the year. There are numerous commercially-available CBM tasks with demonstrated reliability and validity that are used within MTSS—such as CBM tasks for universal screening multiple times a year in fall, winter, and spring (e.g., Christ et al., 2010). Several studies on classwide reading interventions have used CBM tasks as the primary means to identify when a classwide intervention is needed. For example, researchers have used oral reading fluency CBM tasks to identify classrooms for classwide intervention (Burns et al., 2014, 2016).

Because CBM tasks address a large scope of skills, they can provide information on student progress across an entire year; however, this large scope may make identifying specific areas of difficulty more challenging. For this reason, it is likely preferable to consider student performance on both CBM tasks and classroom CBA tasks.

Decision Rules

Components and Examples

After gathering data from relevant assessment tasks, it is important to have a formal decision rule that specifies if an intervention is to be delivered or not. A decision rule has at least three components: (1) student data, (2) a cut score, and (3) a recommendation for next steps. A definition of each component and an example sentence frame for decision rules are provided below.

Decision Rule Term	Definition
Student Data	Student data may include student performance on relevant CBA and CBM tasks (e.g., rate of words read correctly per minute).
Cut Score	A cut score is a specific value that separates two categories of reading performance (e.g., low risk versus at risk or proficient versus non-proficient). Cut scores are found from large-scale administration of a CBM task.
Recommendation for Next Steps	A recommendation for next steps is a formal “yes” or “no” decision on an additional tier of support.
Example Decision Rule Sentence Frame
The classroom median score was _______ (student data), and the cut score for the screening window was _____ (cut score), so a Tier 1.5 intervention is ______(recommendation for next step).

Gathering Assessments and Calculating Data

There are specific steps to complete each fill-in-the-blank item above.

For student data, gather data from administered CBA and CBM tasks.

For the cut score, consult published norms of expected performance on the selected CBM task. Cut scores are a numeric score that indicates the difference between proficient reading and non-proficient reading (Truckenmiller & Brehmer, 2021). In well-researched CBM tasks, specific cut scores are identified through large-scale administration of a CBM task, determining how the scores vary by student (e.g., the number of words read correctly at the 10^th percentile, 25^th percentile, and 50^th percentile), and further investigation into the sensitivity and specificity of how specific scores (e.g., scores at the 40^th percentile) predict reading on other established measures of reading (Ysseldyke et al., 2023).

Importantly, cut scores and the categories describing performance below it (e.g., below benchmark, increased risk, or below proficient) vary by CBM task. For this reason, it is critical to consult research or technical manuals for the specific CBM task used. This information can often be found from the CBM publishing company. For example, Illuminate Education, publisher of FastBridge CBM tasks, includes research information on their website.

This step can be time consuming, so we created a free, Tier 1.5 calculator that references the published norms for several common types of CBM tasks (i.e., 2021 FastBridge, DIBELS 8, 2020/Version 3 easyCBM). We describe how to use the Tier 1.5 calculator later in this post.

For recommendations for next steps, a formal decision is made on the need for an intervention (i.e., “yes” or “no”). As noted previously, a unique feature of Tier 1.5 is the need to consider data points for an entire class and not just data points for an individual student. This brings up an important distinction between decision rules for individual students (i.e., Tier 2 and Tier 3) and decision rules for entire classrooms (i.e., Tier 1.5).

Student and Classroom Level

In decision rules at the student level, data from each student are compared to a cut score. If data from an individual student fall below a cut score, then they are recommended for an additional tier of support (e.g., Tier 2 or Tier 3). For example, in several studies, all students who scored below the 25^th percentile on multiple measures qualified for Tier 2 support (e.g., Simmons et al., 2007, 2011). Regardless of the type of CBM task used, decision rules for individuals are used to identify individual students needing support.

In decision rules at the classroom level, data for all students in a class are compared to a cut score. In other words, the decision rule applies to all students in an entire class and not just individual students or small groups. A simple decision rule of greater than 50% of a class scoring below the cut score (i.e., below benchmark or below proficiency) on a CBM task may be sufficient to make a recommendation for a classwide intervention.

Alternatively, a finer grain calculation has been done in several studies (e.g., Burns et al., 2014; Maki et al., 2021). In these studies, researchers calculated the median score of the class and compared it to a cut score, such as the 40^th percentile. Calculating the median score for a decision rule can be done in a straightforward manner by sorting student data from high to low, then selecting the middle or median value.

As an example (Burns et al., 2016), researchers in one study calculated the median score for two classrooms on an oral reading fluency CBM task: The median score for Class 1 was 23 words read correctly, and the median score for Class 2 was 18 words read correctly. The benchmark for the selected CBM task was 35 words read correctly. Because these two median scores fell below the benchmark, a classwide intervention was implemented.

Tier 1.5 Calculator

To better understand the Tier 1.5 process and decrease the time needed for DBDM, the Iowa Reading Research Center has developed a new Tier 1.5 calculator for commonly used CBM tasks from three publishers: 2021 FastBridge, DIBELS 8, and 2020/Version 3 easyCBM. This free, user-friendly application allows educators to upload CBM task data for an entire class and compare it to two percentile ranks or cut scores used in prior studies to identify individual students for reading intervention, the 25^th percentile (e.g., Truckenmiller & Brehmer, 2021), and entire classrooms for classwide intervention, the 40^th percentile (e.g., Burns et al., 2014; Maki et al., 2021). Then, the calculator automatically does two things: It calculates the median score for the class and compares the median score to published norms from large-scale administrations of the assessment (Renaissance Learning Inc., 2021; University of Oregon, Center on Teaching and Learning, 2022; Riverside Assessments LLC., 2020).

Access the Tier 1.5 Calculator here.

Conclusion and Next Steps

In conclusion, there are a variety of considerations for determining if Tier 1.5 is needed. This includes gathering student data from relevant CBA and CBM tasks, identifying appropriate cut scores, and determining a recommendation for next steps. More research is needed on these considerations (e.g., most useful cut score), but educators can use the Tier 1.5 calculator to understand these types of decisions in a straightforward and quick manner.

While exploring the need for Tier 1.5, there are at least two other final considerations. First, we did not focus on Tier 1 core instruction. While determining the need for Tier 1.5, it is beneficial to also consider the overall performance of Tier 1 to promote overall reading growth. If a substantial number of students have low scores on the CBM task, it is possible issues with Tier 1 may need to be resolved first before a classwide intervention can be beneficial.

Second, we did not focus on different types of classwide interventions. In several prior studies, educators used peer-assisted learning strategies (PALS) to improve student reading performance (e.g., Maki et al., 2022). More information on PALS can be found on the What Works Clearinghouse.

References

Burns, M. K., Karich, A. C., Maki, K. E., Anderson, A., Pulles, S. M., Ittner, A., McComas, J. J., & Helman, L. (2013). 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. (2016). Assessment-based intervention frameworks: An example of a Tier 1 reading intervention in an urban school. In S.L. Graves and J. J. Blake (Eds.), Psychoeducational assessment and intervention for ethnic minority children: Evidence-based approaches (pp. 165–182). American Psychological Association. https://psycnet.apa.org/doi/10.1037/14855-010

Burns, M. K., Haegele, K., & Petersen-Brown, S. (2014). Screening for early reading skills: Using data to guide resources and instruction. In R. J. Kettler, T. A. Glover, C. A. Albers, & K. A. Feeney-Kettler (Eds.), Universal screening in educational settings: Evidence-based decision making for schools (pp. 171–197). American Psychological Association. https://doi.org/10.1037/14316-007

Christ, T. J., Silberglitt, B., Yeo, S., & Cormier, D. (2010). Curriculum-based measurement of oral reading: An evaluation of growth rates and seasonal effects among students served in general and special education. School Psychology Review, 39(3), 447– 462. https://psycnet.apa.org/record/2010-22344-010

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Fien, H., Nelson, N. J., Smolkowski, K., Kosty, D., Pilger, M., Baker, S. K., & Smith, J. L. M. (2021). A conceptual replication study of the enhanced core reading instruction MTSS-reading model. Exceptional Children, 87(3), 265–288. https://doi.org/10.1177/0014402920953763

Hosp, M. K., Hosp, J. L., & Howell, K. W. (2016). The ABCs of CBM: A practical guide to curriculum-based measurement. Guilford Publications.

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Kovaleski, J. F., VanDerHeyden, A. M., Runge, T. J., Zirkel, P. A., & Shapiro, E. S. (2022). The RTI approach to evaluating learning disabilities. Guilford Publications.

Maki, K. E., Ittner, A., Pulles, S. M., Burns, M. K., Helman, L., & McComas, J. J. (2022). Effects of an abbreviated class-wide reading intervention for students in third grade. Contemporary School Psychology, 1–9. http://dx.doi.org/10.1007/s40688-020-00343-4

Renaissance Learning, Inc. (2021). FastBridge score to percentile conversion tables [PDF]. https://renaissance.widen.net/view/pdf/kna2idquls/FastBridge-Score-to-Percentile-Conversion-Tables_2024.pdf

Riverside Assessments, LLC. (2020). easyCBM® detailed percentile lookup tables (Version 3, updated July 1, 2020) [PDF]. https://lficheweb1.d91.k12.id.us/WebLink/DocView.aspxid=275975&dbid=0&repo=SD91LFData&cr=1

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Truckenmiller, A. J., & Brehmer, J. S. (2021). Making the most of Tier 2 intervention: What decisions are made in successful studies? Reading & Writing Quarterly, 37(3), 240–259. https://doi.org/10.1080/10573569.2020.1768612

University of Oregon, Center on Teaching and Learning. (2022). Dynamic indicators of basic early literacy skills (DIBELS®) 8th Edition: Zones of growth technical report [PDF]. https://dibels.amplify.com/docs/techreports/DIBELS8-Zog_tech_report_20221010.pdf

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