data tool

In facilitating teams of teachers who are focused on using their data to figure out next steps for instruction (or school level teams focused on teaching and learning), Using Data facilitators introduce processes and protocols to support genuine inquiry.  There are the 5 phases of continuous improvement (or the 6 or the 8). And frequently schools implement cycles of improvement.  What they so frequently miss is one element that makes it work.  In music, it’s “all about the bass”.

In data analysis it’s all about discovery,  being open, being in exploration mode, which means leavimultiple pieces of large chart paper displaying data analysis that creates a hand-drawn data wallng assumptions at the door. The tension here is that as humans, we aren’t that comfortable with holding out in uncertainty.  We want to solve problems quickly. We want to feel confident that we know what we’re doing. And any suggestions to the contrary, render us incapable to doing anything but sticking to what is familiar instead of taking the risks that high performing schools have come to relish.

If we extend the notion of being open a little further, it isn’t too far a stretch to realize that  along with discovery and exploration goes one of the 7 Norms of Collaboration – screen-shot-2016-12-01-at-10-21-07-am“Presuming Positive Presuppositions”. In other words, assume that everyone at the table only wants what’s best for our students. And most importantly, when looking at our students’ results, presume that every student wants to learn and to be successful. If we can presume positive presuppositions about them while we stay in discovery mode to learn more about their strengths, their sometimes hidden or buried aspirations, we can figure out how to design instruction that overwhelms the effects of poverty, learning disabilities and language differences.

In other words, explorers don’t let students’ historical and demographic profiles bias their instruction. Instead they are continuously open to the possibilities that are within every student we teach. Teacher teams who have learned how to confront their low expectations for student learning use the data to surface the questions leading to the next great discovery rather than jumping to premature conclusions that typically result in same old, same old – cycles of reteaching, assigned interventions and test prep.

On another note, with this week’s announcement by President-Elect, Donald Trump that his nomination for the Secretary of Education position is Betsy DeVos, a strong advocate of education vouchers and charter schools in Michigan, perhaps we could slow down any rush to judgement and instead, benefit by using some of the same processes for using data effectively (be in discovery mode, triangulate the data, search for root causes, monitor progress toward goals)  before we draw conclusions about the implications of this appointment.

GUEST BLOGGER: Mary Anne Mather, Using Data Facilitator & Social Media Liaison on Twitter & FaceBook

“Make data observations. Then generate possible explanations that inform next-steps to finding the best teaching and learning solutions.”
(from: Love, Nancy et al. The Data Coach’s Guide to Improving Learning for All Students, 2008.)

drawing of a figure with a question mark and thought bubbleData analysis is more effective, and more on-target for getting student achievement results, if a team of stakeholders first observe and list as many details as possible about what the data reveal, followed by making inferences about these observations, and then asking “why is this happening?” “what else do we need to know to be sure?”.

Infer/Question is the fourth stage in a team-based, 4-phase dialogue process* that guides deep discussion toward deriving accurate meaning from performance data. (See more information about Step 1: Predict, Step 2: Go Visual, and Step 3: Make Observations.)

These action steps will help you and your data team share inferences about the story the data reveal—inferences that will inform important next-steps toward identifying a valid student learning problem and its true causes. (more…)

By Diana Nunnaley, Director, TERC’s Using Data

Depending on where you sit, and which frame of reference shapes your work, you either celebrate charter school efforts or think charters reflect a “right” wing or “left” wing  (take your pick) conspiracy to undermine the role of public education in the United States.

A blog post is too short a space to weigh into the considerable arguments both pro and con that can be made regarding the place for charter schools in America. To my thinking, charters are a natural consequence of Americans seeking a solution to a social problem. We may not agree on the substance of the problem or the direction of the solution, but in a society that values and applauds entrepreneurial efforts, charters are here to stay. That is, they have a place until we learn more about the experience (hopefully by examining the data) or, have a collective epiphany about the impact of poverty on kids’ success in learning and activate the collective will to change the way we fund and support local education.dictionary page with definition of the word data somewhat out of focus

Charter School Vision Equally Blurred

Based on my experience working in schools across the country, the reality is that teachers in charter schools bring the same passion and desire to help children learn as teachers in any other public or private setting. They face the same staggering challenges and then some. And they bring the same blind spots to the table when examining their student learning data. (more…)