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What data-based decision making in education looks like

By Jasa Todorovich. Last updated Sep 27, 2026

For teachers and school leaders: Lorsey reads the IXL, NWEA MAP and Savvas scores your school already has and shows the next step for each student. See it on your own roster

The short answer

Data-based decision making in education means using the data a school already collects to decide what to teach, how, to whom and with what support. The Center on Multi-Tiered System of Supports at the American Institutes for Research (the MTSS Center) defines it as the use of screening, progress monitoring, and other forms of data to make decisions about instruction, movement within the multi-level prevention system, intensification of instruction and supports, allocation of resources, and identification of students with disabilities, in accordance with state law.

The most practical description for a classroom is a cycle. The What Works Clearinghouse (WWC) practice guide Using Student Achievement Data to Support Instructional Decision Making (Institute of Education Sciences, 2009) lays it out in three steps:

  1. Collect and prepare a variety of data about student learning.
  2. Interpret the data and develop hypotheses about how to improve student learning.
  3. Modify instruction to test those hypotheses and increase student learning.

Then the cycle starts again: new data shows whether the change worked.

How the same idea runs inside a multi-tiered system of supports, with screening, progress monitoring and written decision rules at each tier, is covered in MTSS data: what to collect at each tier and how to read it. This guide covers the cycle a teacher or a grade-level team runs.

Where the decisions are made

The MTSS Center says data-based decision making occurs at all levels, from individual students to the district. The WWC guide splits its five recommendations the same way:

Level WWC recommendation
Classroom 1. Make data part of an ongoing cycle of instructional improvement
Classroom 2. Teach students to examine their own data and set learning goals
School 3. Establish a clear vision for schoolwide data use
School 4. Provide supports that foster a data-driven culture within the school
District 5. Develop and maintain a districtwide data system

The guide is candid about its evidence. Its panel rated the evidence for all five recommendations as low, because it could not point to rigorous studies showing that these practices raise achievement. The recommendations are the panel's best advice, informed by experience and research. The WWC page for the guide now lists each one as minimal evidence.

Step 1: collect and prepare a variety of data

The guide's first point is that no single assessment provides all the information a teacher needs. It names sources schools usually already have: annual state assessments, district and school assessments, curriculum-based assessments, chapter tests and classroom projects.

Each source answers a different question.

  • Annual state tests show broad strengths and weaknesses and help set goals, but time has passed since they were given, so the guide suggests gathering more information at the start of the year. It also warns that leaning on one high-stakes test can lead to teaching to the test and to gains that do not show up on other assessments of the same content.
  • Interim assessments are given routinely, such as each semester, quarter or month, and in a consistent way across a grade or subject. Their results are comparable across classrooms, and they are frequent enough to track the progress of this year's students.
  • Classroom data, such as unit tests, projects, classwork and homework, can be gathered quickly and gives detailed examples of student work. Its weakness is that assignments and scores are not generally comparable across classrooms.
  • Nonachievement data, such as attendance records, records of parent meetings, classroom behavior charts, individualized educational plans (IEPs) and prior data from students' cumulative folders, helps interpret the test results.

Preparing the data means putting it in a form that answers a specific question. The guide's example is a teacher who graphs four waves of interim math results with one line per performance quartile from last year's state test, to see whether the students who started behind are closing the gap. It adds that each group needs enough students so that one or two outliers do not drive the picture.

Step 2: interpret the data and form a hypothesis

The guide suggests two goals for interpretation: find each class's overall areas of relative strength and weakness, so time and resources go to the most pressing content, and find each student's strengths and weaknesses, so assignments and feedback can be adapted.

The method it names is triangulation: using several data sources on the same question, each one confirming or tempering the others. When a state test and a district interim test show the same weak skill, a teacher can be more confident about where to focus. When they disagree, the guide says to look closely at the items on both tests to find the source of the discrepancy.

A hypothesis is only useful if the next round of data can test it. The guide lists three characteristics of a testable hypothesis:

  • it names a promising instructional change and the effect you expect;
  • the effect can be measured;
  • there is comparison data from before the change.

Its example: more than half of a 3rd grade class struggles with subtraction, so the teacher hypothesizes that teaching the "trade first" method will help, and plans to compare the students' subtraction scores on the interim assessment before and after.

The panel recommends interpreting data together, in grade-level or department teams, so that teachers share practices and build common expectations for student work.

Step 3: change instruction, then check

The guide lists the kinds of changes teachers make at this step:

  • more time for topics students are struggling with;
  • reordering the curriculum to shore up essential skills;
  • grouping or regrouping students for help with particular skills;
  • new ways of teaching difficult concepts, often from colleagues;
  • better aligning expectations between classrooms or grade levels;
  • better aligning what the curriculum emphasizes across grades.

It suggests taking notes on how students responded during or soon after the change, then collecting and interpreting new data to evaluate it. That new data is the start of the next cycle.

When the cycle is about one student

For a student who needs more intensive support, the National Center on Intensive Intervention (NCII) describes a more intensive version called data-based individualization (DBI): a research-based process for individualizing and intensifying interventions through the systematic use of assessment data, validated interventions, and research-based adaptation strategies. Its steps run from a validated intervention program through progress monitoring, diagnostic assessment and intervention adaptation, and back to progress monitoring. NCII calls DBI the technical term for what many good teachers do naturally when they review student data and change their teaching based on what works.

Too much data, not enough focus

The first obstacle the WWC guide addresses is teachers who have so much data that they do not know where to look. Its answer is to start from a specific question and identify the data that answers it. School leaders can help by setting schoolwide goals that make clear which data matters.

Triangulating across vendors starts with knowing what each number means, because an IXL Diagnostic level and a NWEA MAP Growth RIT score are not on the same scale. The guides on the IXL Diagnostic and the NWEA MAP RIT score explain each one.

Where Lorsey fits

Lorsey combines learning signals from connected curriculum and assessment systems and helps schools decide what each student should work on next. It does not replace those systems or provide its own curriculum.

Frequently asked questions

What is data-based decision making in education?

It is the use of screening, progress monitoring, classroom and other data to decide on instruction, support and resources. The MTSS Center lists decisions about instruction, movement between tiers, intensifying support, allocating resources and, under state law, identifying students with disabilities. It happens at every level, from a single student to a whole district, and the What Works Clearinghouse describes the classroom version as a repeating cycle of collecting data, forming a hypothesis and changing instruction.

What are the steps of data-based decision making?

The What Works Clearinghouse practice guide describes a cycle of three steps. First, collect and prepare a variety of data about student learning. Second, interpret the data and develop hypotheses about how to improve learning. Third, modify instruction to test those hypotheses. New data then shows whether the change worked and starts the next cycle. The guide notes that a teacher can enter the cycle at any step, with a question, a hypothesis or a change to evaluate.

What data do teachers use to make decisions?

The What Works Clearinghouse guide names annual state assessments, district and school interim assessments, curriculum-based assessments, chapter and unit tests, projects, classwork and homework. It adds nonachievement data such as attendance records and students' cumulative folders to help interpret test results. It says no single assessment provides all the information a teacher needs, because each source has its own strengths, limitations and timing.

What is triangulation in data-based decision making?

Triangulation is using several data sources to answer one question, with each source confirming or tempering the others. When a state test and a district interim test show the same weak skill, a teacher can act with more confidence. When they disagree, the What Works Clearinghouse guide says to look closely at the items on both tests to find the source of the discrepancy, and to use classroom work to see which part of the skill students need help with.

Is there evidence that data-based decision making improves achievement?

The What Works Clearinghouse panel rated the evidence for all five of its recommendations as low. It could not point to rigorous studies showing that the practices raise student achievement, and it found no study of the inquiry cycle's effect on achievement. It presents the recommendations as its best advice, informed by experience and research, and its summary page now labels each one as minimal evidence.

What is the difference between data-based decision making and data-based individualization?

Data-based decision making covers decisions at every level, from a student to a district, and includes screening, progress monitoring and other data. Data-based individualization, or DBI, is the National Center on Intensive Intervention's process for intensifying intervention for an individual student. It starts from a validated intervention program and uses progress monitoring, diagnostic assessment and adaptations of the intervention, then returns to progress monitoring to check the student's response.

Sources

  • Institute of Education Sciences, What Works Clearinghouse, Using student achievement data to support instructional decision making (PDF), September 2009, panel chaired by Laura Hamilton. Source for the five recommendations and their levels of evidence, the data use cycle, the data sources and their trade-offs, the interim assessment characteristics, the graphing example, triangulation, testable hypotheses and the subtraction example, collaborative interpretation, the kinds of instructional changes, and the roadblock of too much data.
  • What Works Clearinghouse, practice guide summary page. Source for the current evidence labels.
  • Center on Multi-Tiered System of Supports at AIR, Data-based decision making. Source for the definition and the levels at which decisions are made.
  • National Center on Intensive Intervention, Data-based individualization. Source for the definition of DBI, its steps, and its relation to what teachers already do.

Lorsey is not affiliated with or endorsed by the U.S. Department of Education, the American Institutes for Research, the MTSS Center, the National Center on Intensive Intervention, IXL Learning or NWEA. Their public materials and product names are cited here to describe practices and tools schools already use.