In today’s classrooms — especially those serving diverse learners — we know that “one size fits all” doesn’t cut it. When teachers and schools use evidence from student performance and progress, they’re better able to tailor instruction, group learners meaningfully, and meet each student where they are. That’s the power of data-informed, student-centered instruction.
Below are three key resources that illustrate how to use data well: from grouping students to creating personalized lessons.
Using ThinkCERCA Data to Group Students
Using Thinkcerca Data to Group Students
What it is: A tool / presentation showing how to leverage data from ThinkCERCA assignments to form groups among students based on performance, skill levels, or specific needs.
Why it's powerful: Because ThinkCERCA already captures writing, reading, and reasoning performance data, this resource helps teachers convert that raw data into actionable groupings — small-group instruction, peer review circles, remediation or enrichment groups, etc. It turns abstract data into concrete instructional decisions.
How to use it:
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Use the data to identify students who share needs (e.g., struggling with argument structure, grammar, reading comprehension).
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Group students flexibly — maybe homogeneous groups for targeted support, or heterogeneous groups for peer learning.
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Re-assess and re-group periodically, based on updated performance, rather than fix groups for the whole semester.
Using MAP Data to Group Students
Using MAP Data to Group Students
What it is: A spreadsheet-based tool (linked via Google Sheets) for grouping students using data from MAP Growth (or similar standardized/diagnostic assessment data).
Why it’s useful: MAP (or other diagnostic) data provides a snapshot of where students are academically — above, at, or below grade level — which helps teachers understand not just who needs support, but in which skills or domains. This makes grouping more precise than “just by grade level.”
Example of practice: One common pattern is to divide students by percentile bands (e.g., bottom 40%, middle, top 25%) and then group by instructional area (vocabulary, reading comprehension, skills mastery) rather than just overall score. blendedresources.org+1
How to use it:
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Use the spreadsheet as a dashboard: input MAP scores, look at overall and sub-scores, identify clusters of need.
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Based on those clusters, design instruction or interventions targeting students grouped by need (e.g., vocabulary support, comprehension practice, enrichment for advanced readers).
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Periodically reassess and shift groups as students grow — flexible grouping helps keep instruction aligned with evolving needs. dpdol.nwea.org+1
Using Personalized Skills Lessons (Video)
What it is: A video walkthrough (via Vidyard) that shows how to use personalized, skills-based lessons — likely tied to data from assessments or diagnostic tools — to tailor instruction for individual students or small groups.
Why it matters: Even with groupings, students’ learning needs are unique. Personalized skills lessons let teachers provide bespoke instruction — for remediation, skill-building, or acceleration — rather than relying purely on whole-class pacing. This is especially useful for readers or writers who need focused work on grammar, structure, comprehension, or reasoning.
How to use it effectively:
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Use data from ThinkCERCA or MAP to identify which skills or standards a subset of students need to focus on.
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Assign personalized lessons targeting those exact needs.
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Monitor progress, use formative assessments to gauge growth, and adjust lessons or regroup as needed.
This aligns with research on differentiated instruction: using data to drive not only grouping, but paths of learning tailored to each student.