Reliable, evidence-based assessment tools are at the core of a strong structured literacy program. Data must drive decisions about instructional next steps for students.

We can use data for decision-making at multiple levels. First, examining individual student data helps identify specific areas where a student may need additional support or targeted instruction. At the same time, looking for patterns in class-wide data can tell us about the overall health of Tier 1 instruction, helping to reduce reading risk for large groups of students. Only using screening data to identify students who need support misses a powerful opportunity to strengthen our system for all learners.

To make the most of our data, we use a structured process called collaborative problem-solving. This approach helps teams work together to identify a problem, analyze root causes, plan targeted next steps for instruction, and evaluate the impact. This cyclical process allows us to continually refine our instruction, meeting the needs of all students.