How to Become a Data-driven School

“Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students.” Data-driven instruction – Wikipedia

Educators also talk about data-informed or evidence-based instruction. All these terms label the practice of turning data into actionable information to guide both learning and teaching.

To support this practice of data-driven instruction, there are a few things that happen at the system or school level:

• Developing a culture of data use

• Using high-quality assessments

• Teaching high-quality curriculum

• Planning assessments (a balanced assessment system) and the conversations focused on the results

If you happen to be an educator in a school or system without all the above, don’t get discouraged. You can still make your instructional practice informed and guided by data. And when we say data, you are not limited to assessment data.

Why implement Data-driven instruction?

Data helps us understand our students. It helps educators identify the knowledge, skills, needs, interests, and levels of understanding of their students. Data comes from many sources – demographics, attendance, transportation, academics, behavior, surveys, etc. Each data set has an intended purpose; often these purposes can intersect. When educators (and learners) clearly understand the purpose of each data set and how it can support learning, instructional guidance is possible.

A culture of data use involves both educators and learners. Part of our responsibility as educators is to make sure we know how to use that data and that we teach our learners to use their data to support and guide their learning. Consider it a moral responsibility. How many facets of your life have data that guides (informs/drives) the decisions you make? The direction you take? Why not start with data that students have control over?

How to implement Data-driven instruction

Data-driven instruction is pretty simple. There are three main steps:

1. Collect: Collect only the data you need (ex. class assessments (informal and formal), standardized test results, teacher observations).
2. Analyze: Clarify essential and non-essential information. Look for patterns. Consider historical trends for either the grade or group.
3. Act: Create lesson plans. Transparently explain to learners the direction you are headed, why, and what they need to do to be successful.

These three simple steps are a habit loop that feeds a culture of data use.

Now let’s break down the action portion of the process.

• Build capacity (teacher and students): Set up routines and habits that help you build capacity for this work. An example of this is setting aside 20 minutes every Friday for students to reflect on their learning habits for the week – did they ask questions? Did they participate? 

• Identify the goals: You must know where to start, and then identify gaps and opportunities for growth. Set goals that are visible for students. Becauseteachers aren’t the only ones involved in collecting and analyzing student data, everyone needs clarity on the goals. Make the goals visual and teach learners to track their progress. Plan time with learners for conversation about interpreting and analyzing their data to set their learning goals. Then plan time for learners to reflect and self-assess.

• Create a culture of data use: Transparently using data for and with students helps establish this culture of data use.

• Choose the right data: Use data for its intended purpose. Standardized tests gauge overall knowledge. Classroom level tests dig deeper into specific content strands or standards to confirm these findings and provide more individualized results. These individualized insights allow goals to be modified and even curriculum to better meet the class needs. Classroom assessments might even lead teachers to revise or restructure materials or choose different teaching/assessment strategies. Formative instructional practices guide quick modification of the current or next lesson and help identify gaps before a summative or standardized assessment is given. These practices also allow learners to adjust what and how they learn while learning.

• Turn data into action: Start with a strengths-based approach. What are the strengths of your class? Is a general strength a strength for all subgroups? What strategies do you use to teach that strength area? When we identify patterns, we can start to change them.

If you’re new to data-driven instruction, start small. Build new habits:

• Start with one subject or class

• Narrow your focus to the key concepts/skills you want your learners to gain

• Identify what data you need to collect and how you will organize it

Consider – Can I get the data I need by asking three questions at the end of the lesson or the end of the week, etc.? Your formative instructional practices will be a big part of your data collection. Triangulate your formative data with your summative data and observations. Concepts that need reteaching or additional support will surface. You’ll know if students need acceleration or remediation. Your use of data should inform adjustments to lessons and instruction. Because of your transparent use of data, students should also know where they are in their learning and what adjustments they need to consider or make.

Data Challenges and Benefits

There are a few common challenges to adopting the habits of data-driven instruction.

• Too much data can be overwhelming – this is why focusing on specific skills is a good way to start.

• Not knowing how the results of a specific assessment are to be used or misunderstanding the data – We can be well-intentioned, yet if we truly don’t understand the purpose of an assessment or how to correctly interpret the results, we may hurt student learning. If you need to check your understanding, ask a colleague or administrator.

• Time – This is where the three steps become a habit – collect, analyze, act.

Data-driven instruction, done transparently, has a place in every aspect of a lesson.

• Whole group: Using summative and standardized assessment results, educators can start a lesson where most of the class needs more instruction. Pre-assessment and micro-lessons (or learning stations) can help target even more specific skills.

• Small group: Shared needs and interests can help teachers form flexible, small groups to target skills with specific activities.

• Independent learning: This is where learners can take charge of their learning. In a conversation with the teacher, both reference data that guides what skills, what strategies and what activities the learner will work on, and how progress will be measured.

When teachers use data to drive their decisions and plans, they can respond to problems more effectively, construct new teaching methods, and advance skill sets faster. Current studies indicate that teachers in schools with data-focused programs think using data improves instruction significantly. – The Benefits of Data-Driven Education (methodschools.org)

Don’t forget to analyze your new habits and efforts. Have the steps you’ve taken made a difference in student understanding and academic achievement?

Curious about how to maximize student assessment data? Explore essential strategies for effective data-driven decision making in our article: Data-Driven Decision Making: Three Considerations.

Resources

“Data-Driven is STUDENT-CENTERED”: Collecting Meaningful Data – YouTube

Data-Driven Decision Making | Edmentum

Using Data in Efforts to Transform Teaching and Learning (wested.org)