How to Use Data to Drive Instruction in the Classroom

Instructional tools have changed significantly throughout my decades-long career. Gone are the days of chalkboards, mimeograph machines, pencil-and-paper tests, and even Commodore 64s and Apple IIe. Now, iPhones and ChatGPT are commonplace. I can still recall the scent of those cool sheets of paper and hear the dot matrix printers.

Back then and still today, teachers have little time to think about differentiating lessons, let alone ways to use data to drive instruction and adjust lessons. Teachers today are hard-pressed to revisit material already taught, knowing they have a jam-packed scope and sequence with an ever-pressing pacing guide in front of them. Fortunately, today’s technology, digital resources, and online assessments make differentiating instruction and creating small groups easier.

As I work with educators across the country, it is a common expectation that teachers ground their lessons in grade-level curriculum. However, skills outlined in standards-based resources and curricula assume all students are at the same point in their learning journeys. We know that isn’t the case.

Planning for a classroom of students at different levels and with different needs requires enough time, valuable tools, and various strategies that can guide instructional decision-making.

How Does Data Drive Instruction?

Data-driven instruction is a method of making instructional decisions based on analyzing data. For example, educators can use student data gathered from assessments (such as data about student proficiency against grade-level expectations) to inform instructional decisions. When educators implement a data-driven approach, they can plan instruction and respond to student needs even more effectively. In this article, I walk through four strategies educators can apply to use data to drive instruction.

4 Strategies for Using Data to Drive Instruction

Strategy 1: Chunk Information

Let’s face it: we are awash in data and short on information. Chunking is an effective way to organize a large amount of information into smaller pieces. When thinking about using data to drive instructional decisions, it helps to chunk data into three categories:

• Content
• Instruction
• Assessments

Think of these three categories as the CIA (a different type of intelligence agency) of differentiation decision-making. When combined, they can be used to drive instruction and create a formula for strong student outcomes: (Content × Instruction) + Assessment = Outcomes.

In the formula, the multiplier is the teacher’s data-driven decision-making on curriculum resources and teaching strategies, which is supplemented with assessments to monitor student learning and academic gains over time, tied to skills and learning standards.

Strategy 2: Analyze Content Data

Content refers to data on the materials educators use to teach students. Resources can include the core curriculum text (print or online), supplemental supports for practice and personalized instruction, and intensive intervention resources. Typically, school and district curriculum teams provide these resources. However, many teachers have a set of go-to resources they collect from colleagues and past lessons.

To create a personalized learning path, it is important to assign resources based on what students need. Is the resource going to be used for reinforcement and practice? Perhaps you are trying to extend a student’s understanding, or maybe the student is developing a skill and needs support.

It is helpful to track how frequently a teacher uses a resource throughout the week or during instruction. Tracking the use of resources is helpful to determine if there is alignment among student needs, skill and standard expectations, and learning performance on assessments. To effectively track usage, digital resources should include data reports that are easy to analyze and interpret. Ideally, they also point teachers to effective resources for core, supplemental, and intervention instruction.

Strategy 3: Analyze Instruction Data

Instruction refers to data on the strategies, routines, and pedagogy used during teaching. It is very helpful to monitor both the types of instruction during a lesson or class period and how the time is used.

One way of measuring instruction data is to review lesson plans and estimate the minutes of “teacher talk” versus “student talk.” Or you can review a week’s set of lessons and count the variety of instructional strategies and student activities. Some examples are below.

If you are looking for effective instructional strategies to enhance your teaching, NWEA published a white paper: “The Transformative Ten: Instructional Strategies Learned from High Growth Schools.”

Strategy 4: Create a Balanced Assessment System

Assessments can provide the most helpful data when deciding how to differentiate instruction and select curriculum resources. With the advancement of technology and access to it in the classroom, teachers are using various tools and assessment types to monitor and measure student growth and academic performance. The goal is to have a balanced, coherent assessment system that includes formative, summative, and universal screening/benchmark assessments. An example is below.

The most common type of assessment is formative. Formative assessments are designed to check for understanding, provide student feedback, and guide teacher instruction. This assessment type includes a multitude of quick checks—simple question-and-answer techniques, individual whiteboard responses, formalized structured protocols to share student work, exit tickets, polling during class, or quizzes, to name a few.

Whatever the type of assessment, the data collected must be relevant to instruction and grade-level performance expectations. Reports should provide analysis by strands and individual standards, with item analysis and grouping tools to create small-group instruction and assign targeted resources.

Educators have made tremendous progress using data to drive instruction. In this advanced era of education technology and increased emphasis on individualized learning, differentiating instruction using relevant data continues to be essential for every teacher and school leader. By harnessing the insights derived from student data, teachers can create more tailored and effective instructional experiences, ultimately promoting student success and engagement.

Final Thoughts on Data-Driven Instruction

Based on my experience working with hundreds of district leaders and thousands of teachers, the most important step is having explicit questions in mind before reviewing reports and data. If you don’t frame specific questions, you’ll be wandering aimlessly through data without any purpose or direction and wasting precious time. To successfully implement data-driven instruction, here are four key steps:

1. Collect Relevant Data (qualitative and quantitative)
2. Analyze and Interpret (behaviors, skills, and standards)
3. Create Actionable Insights and Timely Next Steps (lessons and small-group work)
4. Monitor and Adjust (summative and benchmark assessments)

As we continue to navigate the ever-evolving landscape of education, the integration of data-driven practices stands as a cornerstone for fostering excellence in teaching and learning.

How do you use data to drive instruction? Share your strategies with us at shaped@hmhco.com or reach out on Instagram or Facebook.

***

For more on data-driven assessments and instruction, explore HMH assessments that help educators gain a complete picture of student achievement.