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Unlocking Student Potential: A Diagnostic Decision Tree for Screening Data

In structured literacy, data-based decision-making is paramount to ensuring all children meet their right to learn to read. As Ontario moves toward province-wide early reading screening in September, it is important to consider how to use screening data to inform instruction. We screen to improve instruction and outcomes for students, first and foremost!

Each of the three Ministry-approved screeners (Acadience, easyCBM, and AimsWebPlus) consist of multiple subtests that evaluate different essential early literacy skills. To effectively utilize this data, teachers need a systematic method to interpret the results and make informed decisions about instruction. This is where a diagnostic decision tree comes into play.

Diagnostic Decision Tree: A Strategic Approach

ONlit’s Making Sense of Screening Decision Tree is a flowchart that guides educators through the process of analyzing reading screening data. The tree helps in systematically identifying the root cause of a student’s reading difficulties, and building effective instruction or intervention to address this core need.

Early Readers: Building from the Basics

For early readers, we start with the most foundational skills and work our way up. The reasoning behind this is simple: early reading skills build upon one another. If a student has not solidified a basic skill, it will be impossible for them to perform more complex reading tasks effectively. Checking these early foundational skills is a key way to understand reading risk, and to design effective instruction and early intervention.

Struggling Readers: Starting with the Outcome Goal

For older or struggling readers, the approach is reversed. We start at the outcome skill (fluent reading for meaning) and work backward to identify where the breakdown occurs. It is not an effective or efficient use of time and resources to screen every student with a phonemic awareness screener or decoding diagnostic – strategic use of screening data with the help of a decision tree supports educators in testing less and teaching more!

Putting it in Practice

Our Next STEPS in Literacy Instruction book study group used the decision tree to work with student case study data.

Screening data case study

Participants analyzed student screening data with the help of the ONlit decision tree, and then generated an instructional plan with the help of the Next STEPS recommended instructional activities.

Keen to use the Next STEPS text to support strong instruction? ONlit has developed slides to support educators in leading book studies in their own schools or boards. Download the slides, assemble a group of keen educators, and get ready to explore actionable steps to support stronger reading outcomes for all students!

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Nellie Caruso is a grade 1/2 French Immersion teacher in Toronto, passionate about ensuring all children receive evidence-based structured literacy instruction.

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