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Using Data-Driven Instruction in Your Classroom

Data Driven Instruction

There was a time during my teaching career when second- and third-graders wandered aimlessly through books they chose, wrote in notebooks about self-selected topics, and marched through math exercises quickly. The teachers documented their students’ progress in anecdotal notes about their learning behaviors, not their learning progress. They listened to students read and recorded if it sounded “choppy” or “smooth.” Notes were dutifully written in the margins of students’ writing assignments, complimenting them with no mention of writing principles. Pre-assessments were given at the beginning of every math unit, and tests were given at the end. Scores were entered at the end of the unit with no other data because no other information was gathered.

It was a fluid, organic classroom with little information for teachers to learn from, and it spurred my curiosity about data-driven instruction. How could we do better as teachers to understand our students and guide them properly?

In the 1990s, I worked hard to put into practice what researchers were saying about effective instruction as I implemented reading and writing workshops. In a multi-age, multi-ability classroom of second- and third-graders, I studied and implemented best practices for mathematics using pre-assessments to determine individual and group instruction, along with designing tiered assignments for flexible grouping.

Developing new instructional practices was the focus as practices of the last century were being pushed to the wayside, but data collection, analysis, and implications were not truly on my radar until the early 2000s with the introduction of No Child Left Behind.

Since that time, data-driven instruction has increasingly taken center stage and rightfully so. But what is data-driven instruction and why is it so important?

The Importance of Data-driven Instruction

Our sole purpose as educators is to ensure that every student learns. Gathering and using information about what has been learned and what needs to be learned is pivotal to that. Without data, students in schools would be like patients going to doctors’ offices and being prescribed treatment according to their age instead of their symptoms. Too often still, next steps for instruction are determined by set lesson plans, curriculum guides, and textbooks instead of student readiness and need. We need to look at the students as individual learners to provide the best instruction. That’s where data-driven instruction comes in.

Simply put, data-driven instruction is using information gathered from learning results to determine what comes next in instruction. Within a classroom, data comes in two forms: formative and summative. Formative is data is the information gathered as teaching and learning are occurring. Summative data is the information gathered at the end of a learning period, typically a unit of study or a pre-determined period of time. Effective teachers collect both forms of data and analyze it to look for patterns of success and needs.

Oftentimes what our gut tells us as teachers isn’t what the numbers show. Once, I met with a kindergarten teacher who was in her first year of teaching to discuss the reading needs of her students. She brought basic reading data on letter recognition, letter sounds, vowel sounds, CVC patterns, and known sight words to the meeting. After looking at the percentages of what the students knew and didn’t know, it became apparent that the students knew all of their letters and sounds but were struggling with CVC patterns. Specifically, they were having difficulty putting the sounds together to make words. The teacher, who was in her first year of teaching, wanted to focus her instruction on the student memorizing the sight word list. Her reasoning was that she thought sight word recognition was important. The data indicated otherwise.

After some discussion of what the data showed and what research said about early basic reading skills, the teacher left with plans to design activities based on her students’ needs. Had it not been for the data, this group of students would have had instruction focused on what the teacher thought was needed instead of what they actually needed.

Using Data to Design Your Lessons

Data collection and analysis can be overwhelming and time consuming, but it is critical for meeting the learning needs of students. If data-driven instruction is new to you, start small. Here are some steps to ease yourself into data-driven instruction:

  • Begin with one subject
  • Decide what skills and knowledge you want students to know and understand
  • Determine what data you will collect and how you will collect it
  • Narrow your focus on the key concepts/skills you want the students to gain

When considering how to gather the data you want, keep in mind when you will collect it. Will you get the data by asking three questions at the end of the lesson or at the end of four days of instruction, etc.? This is the collection process for your formative data.

After gathering data and analyzing the results, you will know if you must reteach some or all of the concepts or if you will be able to move on. The results will also tell you which students need remediation and which ones need acceleration. The data should inform your instruction and guide your lessons. Adjusting your instruction along the way based on data will improve students’ learning and your end results, or summative data, will be better.

Be informed about the results of your teaching and let data be your guide to better instruction.

If you’re ready to use data to take your teaching to the next level, sign up for the following courses:

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