Fostering a culture of data-driven decision-making takes time and clarity. Consider these small shifts in using data to guide educational practices, administrative decisions, and overall improvements within the school setting.
Clearly define formative assessment and summative assessment expectations.
Provide clear boundaries for teachers as they navigate supporting student understanding in their classrooms and in their PLCs- that support will not always look the same. What are formative and summative assessments used for? How often should they be administered? Are they graded? What purpose do they serve? It is important that a school-community speaks the same language and sends the same message when it comes to understanding the purpose and next steps for varying assessment types. More on these assessment types can be found here.
Establish Clear Objectives for your data work
At the core of any successful data-driven initiative lies a set of clear objectives. Define what you aim to achieve through data analysis for your school, whether it's improving student performance, identifying areas for professional development, or optimizing resource allocation. Specific goals provide a roadmap for your teachers and teams. It creates alignment and keeps the focus when data-overwhelm hits. It is critical that the alignment and importance of data in supporting school goals is not only communicated to varying faculty, but that each faculty member also knows their role and responsibilities in collecting data and which types to prioritize to achieve these goals.
Know your teachers' data-proficiency.
Before you expect teachers to employ data-driven instruction, you have to know their comfort levels with handling data. Inquire about this comfort, their overall philosophies, and their current and realistic use of assessment. This information will help you tailor how to best support them and will build a culture of confidence and consistent usability with assessment. I have a tool that you can use to easily do this. It's called the PLC Insight Survey. It is accompanied by a video that will explain how to administer the survey and what to do with the results. Get it here.
Model Data Drives in PLCs
Once you know where your teachers stand in their data understanding and proficiency, you can model the analysis and next steps of data collection in PLCs. Be present in these meetings as much as it allows. Your teachers need to see that you are part of the data process and will learn from you as you model how to complete a data inquiry-protocol.
Emphasize not just checking for understanding, but The Check for Understanding
The Check for Understanding is a dedicated independent moment for students to showcase understanding with immediate remediation that follows. On the other hand, general checking for understanding refers to whole-group review, question asking with selected student response, circulating and providing feedback, observing student performance, etc. Both of these should happen in a lesson and they should both help determine student proficiency, but the two are not the same. A culture of data is built when teachers are accountable for collecting independent student understanding and closing learning gaps immediately following that moment. That cycle happens during The Check for Understanding. During feedback and debriefs, push your teachers to think about how they can immediately target instruction following a dedicated Check for Understanding.
Ensure Data Accessibility:
Make data easily accessible to all relevant stakeholders. Utilize user-friendly platforms or dashboards that allow teachers and administrators to access and understand the information they need without unnecessary complexity. Accessibility promotes engagement and ownership of the data-driven process. Leaders I work with have had the most success when data is tracked on a spreadsheet or simple data tracker.
Acknowledge and celebrate successes resulting from data-driven initiatives. Whether it's improvements in student achievement or positive changes in teaching practices, recognizing and highlighting these successes fosters a culture of continuous improvement and motivates stakeholders to remain invested in data-driven practices.