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Home Blogs Strategies for using data to deliver differentiated reading instruction to students in early grades
The development of strong reading skills in the early grades plays a key role in students' academic success. To support budding readers, research evidence recommends that teachers provide differentiated instruction in small groups in the context of Tier 1 support. By using differentiated small groups, teachers can better meet the needs of individual students wherever they are in their reading journeys.
In August 2022, REL Midwest held a series of Summer Institute sessions with Battle Creek Public Schools and Lansing School District 158 as part of the work of the Strategies to Improve Reading (STIR) partnership. The sessions provided professional development to K–3 educators, reading interventionists, coaches, and principals on how to use assessment data to identify young readers' instructional needs, organize small groups to meet those needs, and deliver effective reading instruction in small groups.
Using data to inform early literacy instruction is one of three components of an approach that the STIR partnership is developing and testing to improve reading outcomes for young students. Read on to learn more about the STIR data use component and about the importance of using data to form small groups and monitor the progress of students' early literacy skills.
Educators can use universal screening data to gather information about students' literacy skills and help organize students into small groups. Many districts use valid, reliable universal screening assessments in literacy at the beginning, middle, and end of the school year to understand students' strengths and needs. These data can help educators form appropriate small groups to deliver differentiated instruction.1
The results from universal screenings include classroom-level data and individual student data. Educators can use aggregated data to generate a "big picture" of students' strengths and needs at the classroom level. Student-level data can then help identify individual students' strengths and needs, which enable educators to examine patterns in the data and generate groupings of students. For example, teachers can use student-level data to understand the range of skill levels in their classroom. Teachers also can use these data to determine whether specific students may benefit from either remediation on skills that the majority of the class has mastered or exposure to advanced skills that the rest of the class is not yet ready to learn.
Although universal screening data are an essential starting point, teachers also should use diagnostic assessments in literacy to pinpoint specific student needs when forming small groups. For example, after reviewing universal screening data, teachers may still have questions about the skills of individual students. To answer these questions, teachers can look for patterns in the data to identify which students need additional diagnostic assessments to clarify which skills to target. Diagnostic assessments may be standardized tests or informal tests of a student's skills. These assessments provide more in-depth information about an individual student's specific skills to guide future instruction and intervention.2 For examples of diagnostic assessments, check out the resources from the National Center on Intensive Intervention.
Young readers build literacy skills at different rates within both small- and large-group instructional settings. After teachers have used data to form small groups that meet the varying needs of each student, a best practice is to monitor the progress of each student's literacy skill development, particularly for students in high-risk categories. Teachers should then adjust small-group membership and instructional content as needed to respond to the needs of students as they progress in their learning.
To learn more about REL Midwest's work in Michigan, see our blog post introducing the STIR partnership. For more information on how to use data to support literacy success, check out the following resources:
1 Bailey, T. R., Colpo, A., & Foley, A. (2020). Assessment practices within a multi-tiered system of supports (Document No. IC-18). Collaboration for Effective Educator, Development, Accountability, and Reform Center and National Center on Intensive Intervention. https://ceedar.education.ufl.edu/wp-content/uploads/2020/12/Assessment-Practices-Within-a-Multi-Tiered-System-of-Supports-1.pdf
2 Michigan Multi-Tiered Systems of Support Technical Assistance Center. Student assessments. https://mimtsstac.org/evaluation/student-assessments
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