Keep the curriculum in view
Start with the lesson, unit, and standard students are responsible for now. Data becomes more useful when it is interpreted in the context of what students are actually being taught.
Data-driven instruction
Data-driven instruction that ends with a teaching decision.
Data-driven instruction should reduce uncertainty, not create another reporting task. Use evidence to answer four questions: what skill matters now, who needs a different response, what will you teach, and how will you know it worked?
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USE THE REPORTS YOU ALREADY HAVE
Your scores are collected. Your next teaching step should be clearer.
Use supported IXL Score Grid, MAP Growth, aimswebPlus, and classroom assessment exports alongside rosters and curriculum maps. Review learner matches and available evidence before using a suggested group or teaching plan. Accepted formats vary by report.
New beta paths: Star, i-Ready, and mCLASS / DIBELS
Selected CSV or Excel layouts for Star Reading & Math, i-Ready Diagnostic, and mCLASS / DIBELS can provide screening, domain, placement, or benchmark context. Format coverage and the full upload-to-recommendation workflow are still being verified.
These are file imports, not direct vendor connections. Diagnostic scores do not establish mastery of an individual standard or automatically place students in an intervention.
Triangulate evidence
Use assessment results alongside classroom checks, work samples, practice data, and teacher observation when available. Different sources can confirm a pattern or signal that more verification is needed.
Turn patterns into actions
A data meeting should end with an instructional response, not only a color-coded report.
- Priority skill
- Students who share the need
- Specific teacher action
- Student task
- Success check
- Date or point for review
Repeat a short cycle
Teach the selected move, collect another aligned signal, and adjust. A short evidence-to-action cycle is more useful than waiting for the next major benchmark.
How it works
Your report shows a score. Your next lesson still needs a plan.
Stop rebuilding groups and teaching plans from scattered reports. Review the evidence, choose a focused teaching move, and keep a record of the support you provide.
Bring the evidence
Use assessment results, classroom evidence, curriculum pacing, standards, and the class context you already work with.
Find the priority
Identify the skill and students that need attention now instead of stopping at a score report.
Make the next move
Choose support, targeted instruction, extension, or another evidence check—and verify what changed.
Related teacher guides
Keep turning evidence into instruction.
Explore practical guides for grouping, assessment analysis, and next-step planning.
Frequently asked
Common questions
What is data-driven instruction?
It is a cycle of using student evidence to identify an instructional need, choose a response, teach it, and use new evidence to adjust.
Does data-driven instruction mean teaching to a test?
No. Useful data can include classroom tasks, work samples, checks for understanding, practice evidence, and assessments connected to the curriculum.
How does MasteryCue fit the process?
MasteryCue brings curriculum context and student evidence together to surface priorities, flexible groups, and practical instructional next steps.