Using data while teaching is a lot like using a GPS while driving. When you’re using a GPS, you enter the final destination. It’s the place you want to go. Along the way, the GPS will say things like, “Turn left…turn right…exit here.” Based on this information, you adjust your course.

Using data to inform instruction follows the same concept.

At the beginning of the school year, you have a set of standards, learning goals, and summative tests—tools that will indicate whether the students have achieved the goals of that course. But during the year, you need to be able to regularly gauge student mastery to determine if students are on track to meet those goals, or if you need to adjust instruction to meet their learning needs.

Using data based on student assessments—including quizzes, homework, and even informal conversations—will help drive your students toward your class goal.

So, what exactly is data-driven decision making in education, and how can you make the most of this process? Let’s dig a little deeper.

What is data-based decision making in education?

Data-based or data-driven decision making is a system of procedures that teachers use to identify why a student is struggling. Many years ago, we tended to assume that when a student had difficulty in school, it was always because of a disability. What we know now is that there are many reasons kids struggle in school, and they’re not all related to disabilities.

Through very specific procedures, educators can use data to identify the source of student learning needs, address those needs, and then determine whether their efforts are working. 

Data-based decision making starts with universal benchmark screening. This provides data on every student in the school, so teachers can examine it and compare it with other sources of information to identify the students who may need additional help. 

They can then provide additional assistance to those students through various types of intervention (at Tier 2 or Tier 3), and they can conduct progress monitoring in order to see if the student is reaching the learning goals.

Some students may need assistance over a longer period of time, however. They may participate in multiple interventions over multiple grades because of various factors that are affecting their learning. 

Data-based decision making provides tools allowing teachers to do this in a seamless way. It means that support can be provided regularly and immediately, rather than waiting for students to fail.

Why data-driven instruction is part of a healthy school culture

A school with a healthy culture around the data-driven instruction cycle will have teachers in a professional learning community (PLC) or a data team—whatever you might call it—getting together frequently and saying:

“Based on the common assessment that we gave to our students, here’s what my kids had trouble with. I see that your students did really well. What is it that you’re doing that I can now do? What materials do you use? What strategies do you use that I can borrow for my classroom?”

This is one example of data-driven decision making in education. This collaborative culture encompasses the entire framework and puts it together.

Too often, educators think the data-driven instruction process only happens when they review their universal screening data in fall, winter, and spring. In reality, this is an ongoing process that happens with both formal and informal assessment data—including data from classroom formative assessments.

Ensuring that data-driven instruction is used consistently

School or district leaders who want to make sure that all of their teachers and school leaders are using data-driven instruction need to know that leaders at all levels are going to have to be involved.

This is not a process that you can hand off to someone else and say, “Here, go ahead and use this. I heard it works in classrooms.” This is something that needs to be bought into. You can best do this by modeling what the data-driven instruction cycle looks like in a classroom and at a school and district level.

Setting up a data-driven culture in schools

To set up this type of culture in your school, you must thoroughly understand the data-driven instruction cycle. Let’s look at a cycle with three steps: assessment, analysis, and action.

#1: Assessment

According to Paul Bambrick-Santoyo in his book Driven by Data, there are core drivers that should always be considered when developing quality classroom assessments:

• Transparent starting point: The assessments should be written before the teaching starts, and teachers must be able to see them. They are the roadmap. It is not enough to only see the standards.
• Common and interim: They should apply to all students in a grade level and should occur at regular intervals (e.g., every 6–8 weeks).
• Aligned to state tests: They should be aligned to state tests in format and content, and also aligned to the higher bar of college readiness.
• Aligned to instructional sequence: They should be aligned to teachers’ pacing guides so that what is assessed is what has been taught.
• Reassessed: Interim assessments should continuously reassess previously taught standards.

#2: Analysis

Analysis is a process that can be guided and supported by school leaders, but teachers need to own it in order to be effective. There are certain steps they should go through before the test, right after the test, and within one week of the test:

1. Before the test, teachers should review the assessment and make predictions about what they think is going to happen when students take it (i.e., are they well-prepared for the standards or not?)
2. Within 48 hours of giving the assessment, teachers should review the results. Ideally, the students will be involved in this process as well. The ultimate goal of data-driven instruction is to have students work with their own data and take ownership of their learning. After review, students might ask, for example, “Could you provide us with more resources to help with this standard?”
3. Within a week or so, teachers should get together with their PLC, data team, or grade-level team to talk about the standards the students struggled with. They should look for resources within the group and determine what actions they can take to keep students on track to meet their goals.

Key point: It’s difficult to do this without having the test in hand. If I move on to new content immediately after the test has been administered, then there’s no feedback or meaningful review I can do that will lead to a deeper understanding of what my students did and did not learn.

#3: Action

After data analysis on the assessment, it’s time to take action and do something about the results. This can happen in several forums:

• PLCs/collaborative teams: This is where a lot of conversations happen that can improve instruction. Teachers can share best practices, ideas, or resources to help.
• Observing master teachers: This can mean observing teachers in another class, school, or even district. It can be logistically difficult, but it can help teachers learn new practices quickly and benefit from a colleague’s greater experience.
• Content-based coaching/mentoring: Similar to observations, this can leverage educators in the school or be based on external professional development.
• Experimenting with new ideas: Great results can come when teachers are encouraged to try out new lesson plans or activities. When they know that failure isn’t punished, teachers will begin to think outside the box and produce some unexpected results.

Once you’ve taken any one (or all) of these actions, you should assess again. How did that action work? Did students learn what you’re trying to teach them? Are they on track?

Performing these three steps consistently, and then doing it all over again, is the foundation of the data-driven cycle.

Data-driven decision making in action

How should you respond when students don’t learn a new skill or concept? Let’s consider an example using our three-part cycle of assessment, analysis, and action.

Suppose a math teacher gave a homework assignment to students last night covering a new concept she’d just taught (step 1). As she walks around the classroom and listens to the students the following morning, she realizes they struggled with the assignment and did not fully understand the new concept (step 2).

She recognizes that she needs to take action. She might do a quick reteaching session, give additional examples, and/or have the students try to solve the problem in a different way. Then she might call on students at random and ask them to explain what they didn’t understand before (step 3).

This is an example of using the full data-driven instruction cycle in perhaps 15 minutes, based on homework that was given the night before. Clearly, data-based decision making doesn’t have to be an elaborate, time-consuming process. It’s just the teacher gathering data and then responding appropriately to what her students need.

How Renaissance supports data-based decision making

To sum up, not using data while teaching in your classroom is similar to ignoring GPS directions while driving. Might you get to your final destination without the GPS? It’s possible, but it’s not likely. And it will probably take more time than you have available.

Data provides step-by-step instructions that help you to reach your end goal effectively and efficiently.

If you’re facing challenges in gathering and analyzing data in your classroom or school, Renaissance can help. Our comprehensive assessment system includes valid and reliable universal screening and progress monitoring tools, along with standards-based assessment creation and administration for classroom formative and benchmarking.

To learn more about Renaissance assessments and data-driven decision making, connect with an expert today.