Building and Leading a School Culture that Values Data Informed Dialogue to Improve Student Learning
What we know to be true in many schools is that teachers still spend a disproportionate amount of time planning instruction, but don’t place the same emphasis or effort on finding out if the instruction really worked. Perhaps then, less importance has been placed on finding time for teams of teachers, coaches and administrators to take a look at the ‘back end’ — the learning that has taken place as a result of the planning and teaching. We advocate that the input can’t be valued more than the output. Data produced through student assessments is commonly used at a systemic level and for reporting back to parents. “Data” in many schools has been used as an accountability-laden end product — to judge students, to evaluate programs and to rate performance. A rich opportunity exists for teams of teachers to use student learning data not as an end product, but as a tool for developing deep understandings of instructional practices, to shape collaborative approaches to improving student learning, when data is used as a tool for improvement, rather than a final unit of measurement. While this is nothing new to us, the challenge is putting successful structures in place to allow this to happen regularly, and effectively.
In our quest as educators to honor the ‘whole child’, many schools have dishonored the place of empirical evidence in the decision making process. In our desire to value the ‘art’ of teaching, we have devalued the ‘science’ of teaching. We propose that rather than an either/or approach, that we can take a ‘yes, and’ approach to allow us to use learning data to make effective decisions about instructional, program and school improvement. We don’t believe that data detracts from looking at the ‘whole’ child, but in fact is an essential part of that picture.
There is, however, a caveat: that an overuse of data at every turn, may in fact decrease teachers’ natural intuition and flexibility within a dynamic classroom environment. We aim for a healthy balance that honors Marzano’s (2007) ‘art and science’ of teaching — that understanding children, their behavior, the relationship dynamics that impact learning retains equal importance and status as the empirical data that can be analyzed to support intuitive thinking. Susan B Neuman (2016) encourages schools to be ‘data informed’ rather than ‘data driven’ as we seek to make meaning from a broader definition of data that may include test scores, student work samples and observations of behavior, to name a few.
Our work as a leadership team at UNIS Hanoi has focused on the intentional creation of school culture, growth mindsets and open attitudes around effectively understanding and using quality student learning data. Bill & Ochan Powell (2015) strongly advocate for a de-privatization our teaching practice — that the most effective way to improve learning for students is when teachers observe one another, participate in collaborative instructional rounds and engage in meaningful conversations around successful practice. We at UNIS Hanoi extend this thinking to bringing all de-privatized learning data into the realm of ‘ours’ rather than ‘mine’. Just as teams of doctors examine patients together on medical rounds, we are working hard to equip our teachers with the right data and tools to be able to collaboratively analyze, diagnose and create treatment plans for our students that will add value to their learning of every child.
Jim Knight shares his thoughts on the creation Intensive Learning Teams (ILTs) — teams set up for purposeful inquiry, collaboration and shared purpose. Knight describes a number of partnership principles required for effective ILTs: equality, choice, voice, reflection, dialogue, praxis and reciprocity. He also talks about the important role that principals play in this teaming success — principals must understand and support the work that teams are doing. “Most important, this means that the principal must lead change so that what occurs in teams, like ILTs, is designed to address the target.” (Knight, 2011, p. 182)
The establishment of quarterly “Learning Retreats” at UNIS Hanoi aimed to address the need for teams of teachers to gather for purposeful inquiry around student learning. Facilitated by members of the Elementary Leadership Team, with clear meeting structures and protocols in place, our Learning Retreats aimed to meet the following goals: 1) to build the capacity of our teachers to analyze, infer and take actions to improve teaching and learning for students based on a collaborative study of the learning data, and 2) to develop strong, healthy teams who bring conversations about student learning into their regular, collaborative conversations. By working to develop ‘assessment literate’ teaching teams, our hope is that all teachers will feel empowered to use data in a way that helps them to celebrate success, student achievements, and create meaningful plans of action towards instructional improvement. (James-Ward, Cheryl, ASCD, 2013)
Establishing Learning Retreats began with crafting our compelling ‘why’. Simon Sinek (2009) reminds us that “people don’t buy what you do, they buy why you do it, and what you do simply serves as the proof of what you believe.” We also needed to define the ‘what’ and the ‘how’ — what and when data would be collected, how it would be used, and who would benefit from its use. Our criteria was that the data we required teachers to gather, had to be useful in providing information that will help to improve student learning. We aimed for a relatively lean approach, rather than collecting too much. An assessment calendar of common assessments guides teachers as to what, and when, assessment data needs to be gathered.
Another leadership goal for our team has been to empower teachers to be confident consumers of data. In our efforts to achieve this, we used the data visualizations built by the Learning Analytics Collaborative (LAC) to provide us with our data already visualized. Working with the LAC has taken away the need for teachers to produce data — eg. spreadsheets, tables, charts and graphs — many of whom do not come equipped with the skills to be able to do this, which can be a contributing factor to data not being used successfully in schools. Having assessment data turned into useful and easy to read interactive graphs ensures that teachers can immediately get to the work that they are best at — talking about student performance.
As we have set about building our culture of data informed practice, increased shared accountability, and transparency around the learning data of all students, we have been mindful to manage the delicate balance between remaining data informed, and not being too data driven at the expense of all else.
Below, we share a range of tips for effective conversations with teachers around student learning data. These come from the lessons we’ve learned, and the specific actions we’ve taken to build the culture, climate and teacher capacity that are needed to create the dynamic learning community that we desire.
- Create a safe and comfortable meeting environment — de the data to reduce any sense that talking about learning data may make people feel vulnerable and unsafe.
- Assign a facilitator to keep the process moving, well structured, and to monitor the group. I would advocate that this should be the Principal or a key member of your leadership team as long as this doesn’t detract from the aspect of psychological safety (above). The relationship with the facilitator will strongly influence the group’s behavior. If we value the data and are committed to improvement, it’s essential that the leadership team know and understand what they data are saying, and can identify commonalities across grade levels.
- Use protocols. “External structures maximize efficient use of time and increase psychological safety for individual group members.” (Wellman and Lipton, 2004, p. 12). Wellman and Lipton suggest their ‘Collaborative Learning Cycle’. Our preferred tool is the ‘Atlas Looking at Data Protocol’ provided by the National School Reform Faculty.
- Start small. Start with one data set — eg. Grade level reading scores or beginning of year math assessments. Make sure the data is personally meaningful for the participants. It would be best to start with more meaningful internal data, rather than start with external, standardized test data. This can be triangulated at a later time. You will build more trust with teachers by using their data first.
- Consider timing and workload. If a meeting focused around data conversations is ‘another thing on the plate’ — what are you taking OFF the plate in order to ensure that participants have the mental space to deal with this content? At our school, Learning Retreats are scheduled during the school day, class cover provided, and last no longer than 90 minutes. Dates for 4 learning retreats (September, November, February and May) were scheduled at the beginning of the academic year and shared with all teaching teams. Teams know in advance which data sets and student work samples will be needed in advance of each Learning Retreat.
- Have the data already visualized — this takes the ‘hard work’ away from teachers. We do not need or want teachers spending hours creating spreadsheets, graphs and tables. The value of our work with the Learning Analytics Collaborative is that the data are visualized for us, ready for teachers to do their real work — observing, analyzing, and considering implications of the data that lead to action.
- Have the visualized data shared in one location (eg. charts or screen). Do not have teachers using individual computer screens. One location for the data ensures that all participants are looking in the same direction (shared ownership, group safety) which increases the collaboration. “The focus on the third point increases psychological safety, separating the information from the facilitator and allowing group members to talk with and about the data without having to make eye contact with colleagues.” (Wellman and Lipton, 2004, p. 16).
- Document the group’s thinking publicly. As soon as comments are publicly charted, they belong to the group, not the individual.
Ron Berger advocates that students are often left out of the process of using and analysing their own data. A next step for us might be to consider how we move towards transparent use of learning data WITH students, rather than simply FOR students, in a way that is meaningful and relevant.
Following our first two Learning Retreats, we are now ready to gather feedback from teaching teams about how the Learning Retreats may have impacted their planning, instruction, understanding of their students etc. Are we making an impact on school culture? Do teachers feel we are balancing hard and soft data? What changes might we need to make? These are some of the questions we hope that teaching teams will be able to help us answer.
Another of our future goals is to eventually move from Learning Retreats facilitated by an administrator, to having team leaders using the data protocols in team meetings as a tool for looking at all manner of student learning data. Our desire is to see ‘evidence of learning’ meetings become a regular part of what teaching teams do, as is the case for planning meetings. When this is happening on a regular and systematic basis, we will feel like we are making an impact on building and maintaining the culture around data informed practice that we desire.
ASCD. “Code Red: The Danger of Data-Driven Instruction.” Educational Leadership:Disrupting Inequity:Code Red: The Danger of Data-Driven Instruction. Web. 30 Nov. 2016.
ASCD. “The Techy Teacher / Using Data to Personalize Learning.” Educational Leadership:Doing Data Right:Using Data to Personalize Learning. Web. 30 Nov. 2016.
Berger, Ron, Leah Rugen, and Libby Woodfin. Leaders of Their Own Learning: Transforming Schools through Student-engaged Assessment. San Francisco, CA: Jossey-Bass, 2014. Print.
“How Great Leaders Inspire Action.” Simon Sinek: How Great Leaders Inspire Action | TED Talk | TED.com. Web. 07 Dec. 2016.
James-Ward, Cheryl. Using Data to Focus Instructional Improvement. Alexandria, VA: ASCD, 2013. Print.
Knight, Jim. Unmistakable Impact: A Partnership Approach for Dramatically Improving Instruction. Thousand Oaks, CA: Corwin, 2011. Print.
“Learning Analytics Collaborative.” Learning Analytics Collaborative. Web. 30 Nov. 2016.
Lipton, Laura, and Bruce M. Wellman. Got Data? Now What?: Creating and Leading Cultures of Inquiry. Bloomington, IN: Solution Tree, 2012. Print.
Marzano, Robert J. The Art and Science of Teaching: A Comprehensive Framework for Effective Instruction. Alexandria, VA: Association for Supervision and Curriculum Development, 2007. Print.
“National School Reform Faculty” National School Reform Faculty. Web. 30 Nov. 2016.
Powell, William. Teacher Self-supervision: Why Teacher Evaluation Has Failed and What We Can Do about It. John Catt Educational, 2015. Print.
Wellman, Bruce, and Laura Lipton. Data-driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: Mira Via, LLC, 2004. Print.