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Building and Leading a School Culture That Values Data-Informed Dialogue to Improve Student Learning
By Megan Brazil 25-Feb-17
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. We advocate that the input can’t be valued more than the output. 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. While this is nothing new to us, the challenge is putting successful structures in place to allow this to happen regularly, and effectively. We aim for a healthy balance that honors Marzano’s (2007) “art and science” of teaching—that understanding children, their behavior, and 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 the United Nations International School of Hanoi (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 deprivatization 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. Jim Knight describes a number of partnership principles required for effective Intensive Learning Teams (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). Quarterly “Learning Retreats” at UNIS Hanoi were designed 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 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 and 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) and defining the “what” and the “how”—what data, when it would be collected, how it would be used, and who would benefit from its use. To empower teachers to be confident consumers of data, we used the data visualizations built by the Learning Analytics Collaborative (http://www.analyticscollaborative.org/) to provide us with our data already visualized. This ensures that teachers can immediately get to the work that they are best at—talking about student performance. Throughout the process, 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 by depersonalizing the data. • Assign a facilitator to keep the process moving and 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. • Use protocols. Our preferred tool is the “Atlas Looking at Data Protocol” provided by the National School Reform Faculty. • Start small. Start with one data set, e.g. grade-level reading scores or beginning-of-year math assessments. To build teachers’ trust, 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. • Consider timing and workload. At our school, Learning Retreats are scheduled during the school day, class cover provided, and last no longer than 90 minutes. Dates were scheduled at the beginning of the academic year and shared with all teaching teams. • Have the data already visualized. 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 is 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 (e.g., 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 which promotes collaboration, shared ownership, and group safety (Wellman and Lipton 2004). • Document the group’s thinking publicly. As soon as comments are publicly charted, they belong to the group, not the individual. 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? Finally, could we move towards transparent use of learning data with students, rather than simply for students, in a way that is meaningful and relevant? (Berger 2014) After all, our ultimate success is defined by improvement of learning outcomes for all students. References: Berger, Ron, Leah Rugen, and Libby Woodfin. 2014. Leaders of Their Own Learning: Transforming Schools through Student-engaged Assessment. San Francisco, CA: Jossey-Bass. James-Ward, Cheryl. 2013. Using Data to Focus Instructional Improvement. Alexandria, VA: ASCD. Knight, Jim. 2011. Unmistakable Impact: A Partnership Approach for Dramatically Improving Instruction. Thousand Oaks, CA: Corwin. Lipton, Laura, and Bruce M. Wellman. 2012. Got Data? Now What? Creating and Leading Cultures of Inquiry. Bloomington, IN: Solution Tree. Marzano, Robert J. 2007. The Art and Science of Teaching: A Comprehensive Framework for Effective Instruction. Alexandria, VA: Association for Supervision and Curriculum Development. Neuman, Susan B. “Code Red: The Danger of Data-Driven Instruction.” Educational Leadership 75 (November 2016): 24-29, www.ascd.org. NSRF. “National School Reform Faculty.” Accessed November 30, 2016. http://www.nsrfharmony.org. Powell, William. 2015. Teacher Self-supervision: Why Teacher Evaluation Has Failed and What We Can Do about It. Woodbridge, UK: John Catt Educational. Sinek, Simon. “How Great Leaders Inspire Action.” Filmed September 2009. TED video. https://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action. Tucker, Caitlin. “The Techy Teacher / Using Data to Personalize Learning.” Educational Leadership 73 (November 2015): 82-83, www.ascd.org. Wellman, Bruce, and Laura Lipton. 2004. Data-driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: Mira Via, LLC.
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