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How to be a Lead Learner in Your Building When it Comes to Data

In my first experience at a data meeting I was working with my grade level team and we were challenged to identify deficiencies in our instruction by analyzing our students summative data. Our work was well intended, yet like many data teams just starting off, or not guided well, we spent too much time discussing what we thought our students struggled with most, rather than identifying the standards by analyzing real-time data results and making a plan.

Districts want teachers to use data to inform instruction and to report on student progress. But without structure, leadership, and purpose, often being “data-driven” becomes a “data-struggle,” checking a box without actually making continuous improvements that will impact student learning.

How do we lead as principals in a way that models the mindset we want regarding data?

Later in my career I realized the key elements missing in my initial experiences: keeping it focused on students’ learning, being vulnerable about the quality of my own practices, and trying fresh innovative, researched-based approaches; not settling for same old status quo that was not working. My mantra was: identify the best impact for learning, not simply any impact.

As a participant and leader in both situations, here’s what I have learned that can help you take your data meetings to the next level.

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Start with Your Goals

There are real challenges to making data-driven decisions we have to recognize and at times work around. So I recommend identifying your goals first and measuring everything against them.

An example meeting might be to triangulate several data points to make the most sense of a student's picture of learning. The goal of the meeting could be an action-plan, based around needs from various data sets. For example, from a student's most recent progress monitoring result on a specific and key standard, a student's digital supplemental progress on a similar standard, and a recent station/exit ticket from a math block on that standard.

Data (or giving a summary to the district or state) is not the goal itself, but a tool in your instructional belt, helping to build the structure to support a life of learning.

Guide Deeper Analysis with Structured Data Meetings

There is so much data to dig through, where do we even start, and which data point is most important?

Hosting data meetings that are thoughtfully structured and consistent can help you and your team go deeper into data analysis and make an impact on student learning.

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Tips for Structured Data Meetings

  1. Build the team. Build a team of educators who are vulnerable enough to self-reflect on what they can improve around what works best, who will trust and respect other people in the room, and who have a commitment to excellence. It’s okay if everyone doesn’t exemplify all of these traits at first. Start with a small (but strong) team, then add others as the meetings become more structured and build good data habits.
  2. Take notes. Rotate and appoint someone to be the timekeeper and discussion driver; taking minutes is a form of accountability. This will encourage all staff to stay on track and contribute thoughtfully. If you absolutely cannot attend physically, have the meeting shared in a document so you can contribute periodically. By making time for the meeting, whether attending or commenting, you show the importance of the meeting and the importance of reaching the goal: improving learning outcomes for your students.
  3. Establish a formal process to evaluating your data. What we did was identify the key standards or skills the instruction will address. We then measured, designed and developed lessons around these standards. We used the following questions as our guiding principles in data meetings:
    1. What do we want our students to learn?
    2. How will we know that they learned it?
    3. What will we do next with the results?
    4. What are we looking at that will change or adjust our instruction so we can meet the needs of the students?
  4. Model the process as the lead learner. Show a growth mindset and be willing to improve. Highlight what’s working. Jump in and team teach with teachers to support and keep learning fresh. What are teachers already doing well in the classroom that we can see in the data and then observe and share?
  5. Keep it simple. Not all teachers are well-versed in data and statistics. I've found that teachers without data experience do well starting with the reports from ST Math. Teachers are able to explore the reports, identify student progress and usage, hurdles (where students are struggling) and the connected standards for those objectives. Check your edtech tools: do you have one that already organizes the data so teachers can easily make inferences and adjust instruction? Starting with already organized data can help assimilate your teachers to the process and prepare them for looking at bigger data sets later, such as high-stakes standardized assessments.
  6. Keep improving. Don’t stop when the data shows a student knows a concept. What we are looking for is not just a positive impact but the best impact. Go beyond competency to mastery. Just because students have shown they understand a concept doesn’t mean we can’t challenge and enhance their learning. Take it one step further; making connections is what allows for real life application. Ultimately, we want students to develop skills that transfer into successful careers with purpose and a workforce that can solve the future challenges of our society.
  7. Stay open to innovation. Because our tools are getting better and better, who knows where data-driven decision making will go. Edtech is already becoming more and more personalized, in many cases allowing our instruction to be more informed. In addition, students have opportunities to increase ownership of their learning through self-reflection, data tracking, and goal setting. Technology has a huge capacity for change, so let’s lead with excitement for those possibilities rather than frustration at having to change current procedures.

Ultimately, we want students to develop skills that transfer into successful careers with purpose and a workforce that can solve the future challenges of our society.

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Each of us has to own the leadership of our building and practice being the “lead learner.” When you put in the time to do your own research, you’ll be innovating for relevance, practicality, and the outcomes your students most need.

Continuous learning is one of our core values here at MIND Research Institute, and also a personal core value of mine. Please let me know the challenges you have faced with data meetings and how you are working to improve the process. The more we share best practices, the more we can impact student learning on a larger scale!

Further Reading:

  • Learning by Doing “PLC’s at Work” by Richard Dufour

  • Clock Watchers by John McDermott

  • Visible Learning for Teachers by John Hattie

  • The Undoing Project by Michael Lewis

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Brian Coffey

About the Author

Former administrator Brian Coffey successfully implemented innovative STEM education programs in Ohio, and now brings his expertise to districts across the country as Senior Academic Director at MIND. Converse with him on Twitter @PrincipalCoffey.

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