We’ve written here multiple times about collaborative inquiry and how it is the most essential ingredient in using data to achieve real results. The two foundational books that came from the Using Data Project at TERC, Using Data Getting Results* and The Data Coach’s Guide, created a great number of pages, templates and tools describing step-by-step processes to guide instructional coaches, administrators, grade level team leaders and others. The guidance focused on how to begin developing the trust, confidence and skill of practitioners that can bring about collaborative inquiry in the service of learning to use data to guide instructional changes.

Our work began In the late 90’s just as data began it’s ascendency as flash points for improving educational outcomes. The underlying research was initially sparked by requests from schools, districts, and state department of education leaders, who themselves were overwhelmed and seeking help to understand how best to use data in schools. The growing demand for technical assistance led, Nancy Love, then at TERC, to wide-ranging research of a nascent body of literature and a very short list of other educators working in the field – Bob Garmston and Bruce Wellman, Ruth Johnson, Mike Schmoker, Victoria Bernhardt. But in fact, much of what was developed and disseminated through professional development came from schools themselves, where teams of early explorers worked with the Using Data Project team in partnership with WestEd,  to shape the ideas and build the tools.

One result, nearly twenty years later, is that you can’t pick up a book from the educators’ book list without encountering the essential ingredient for any change process to take hold which is collaborative inquiry. And it matters little what the main topic of the book is, to get the work done, it starts with teachers shifting their culture from one based on being individually “highly qualified”, to being part of a collaborative team where inquiry uses multiple perspectives and sets of skills to craft more rigorous lessons, differentiate instruction to assist all children to grow from their own starting point, use multiple forms of formative and summative assessments and other data to measure growth, provide timely feedback to students, and identify new areas of content and pedagogy needing professional development or coaching and peer feedback.

We know better than ever how powerful collaborative inquiry is in the hands of classroom teachers. We have hands-on professional development, online courses and tools, videos from the Teachers’ Channel and YouTube to assist schools in shifting their culture toward one of collaborative inquiry. We have rubrics to help us gauge our progress to achieving the goal and showing us what the end goal will look like. What we don’t have, perhaps, is a clear idea about what it looks like when schools don’t make this journey.

Based on our work over the past twenty years, we recognize what practice without collaborative inquiry looks like, and what it sounds like in schools with no culture of collaborative inquiry at the heart of how teachers go about the business of teaching our children.

Without collaborative inquiry, some if not all of the following attributes are in clear evidence. Same old, same old…

  • There is no weekly schedule that devotes common planning time to teacher teams who need to bring their student work or other assessment results to the table for investigation.
  • Administrators rarely sit in on team meetings to learn and to support teachers’ work.
  • Grade level and subject level team conversations sound like past years’ “teachers’ room talk” where students or their parents are routinely blamed for students’ poor behavior or performance.
  • Cursory examination of end-of-year or interim assessment results is focused on what’s wrong with the wording of the items.
  • From grade to grade, subject to subject, there is no common vocabulary related to assessments, content or pedagogy.
  • Interventions and specialists’ work with students is not aligned or in step with classroom instruction – no coordination of scaffolding to support students’ needs.
  • Students have no idea where their own learning is in relation to the learning standards. Grades are just marks on their work.
  • Instruction is focused on helping students learn “basic skills”, develop fact fluency, use “key words” to determine what algorithm to use.
  • Rigorous instruction is restricted to high achieving students until the rest master the previous bullet items.
  • Team talk rarely if ever digs into the concepts embodied in learning standards, to explore their own content knowledge or the pedagogy that could support different learners.
  • Instructional coaches or assistant principals meet individually with teachers to tell them about the data they are seeing and to suggest needed changes.
  • There is no coherence of content vertically (or horizontally) and little knowledge of what instruction in previous grades looked like relative to current instruction.
  • There is little evidence that current research about best practices or case studies are brought to the table to provide new insights.
  • Administrators select professional development for staff based on what other schools are doing near by.
  • There is no routine monitoring to determine the level of impact of newly implemented “fixes”.
  • No time is devoted to enabling teachers to regularly reflect both as individuals and collectively about their practice, about what excellent instruction would look like to help their students learn this, what would it take, how far are we from that.

We have spent years and years in schools recognizing the attributes above at the outset of the work, and we’ve experienced the satisfaction and real joy that comes when we see these characteristics disappear as teachers themselves begin to surface the important questions and go after the data to help them find the answers.

Collaborative Inquiry is happening! Read about the experience in Metro-Nashville Public Schools working with REL Appalachia to support teachers building their craft together through Collaborative Inquiry.


Johnson, M. (2018). Empowering educators to make data-informed decisions: A district’s journey of effective data use.   In E. G. Mense & M. Crain-Dorough (Eds.), Data leadership for K-12 schools in a time of accountability, 158-183. Hershey, PA: IGI Global, Inc. MNPS Collaborative Inquiry Toolkit website at


*Using Data Getting Results was published by Christopher Gordon Publishers which no longer exists and the book is no longer in print.

*The Data Coach’s Guide is still published and available at Corwin Press a Sage Publishing Co.

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: Jennifer Ungermany colored 3-D question marks

I have worked with so many districts and schools where the leadership proudly points to their “data binders”—most recently I recall a three-inch D-ring binder. Not that binders filled with data aren’t helpful or good, but I caution that if they are not being used to guide instructional and programmatic decisions, well, then they can be a waste of precious time and money. More importantly, if they are not connected to a shared ownership of the questions a group of educators has about instruction and programs and similar concerns, then they can serve no meaningful purpose.

So how do we get from just having data to using data for meaningful change and improved results? (more…)

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

I was bolstered by this bit of news from Tennessee via Learning Forward about the efficacy of teacher teams that meet regularly to share data and strategies. The article is a sound-bite about the good news for student achievement in Wilson County that leaves me hungry for the details about how their meetings are structured,Three teachers collaborating in front of a large chart showing their school improvementy action plan what data they look at, and how that data inform practice. From the published results, they seem to have discovered the perfect storm where collaboration, data, and strategies/solutions meet to make a difference. I, for one—as a facilitator of processes to help conjure similar storms, applaud them!

But the news item also reminded me that there’s more to this kind of success than simply meeting as a team and sharing “what works.” (more…)

Group of people standing on a graph line that is pointing upwardIn early May, TERC’s Using Data Director, Diana Nunnaley, was invited to attend an important national meeting that can have future influence on public awareness, policy, and pre-service and in-service teacher preparation related to data literacy for teachers.

Diana was selected because of the groundbreaking work TERC initiated over ten years ago, developing a process of collaborative inquiry that engages teachers in cycles of data analysis and root cause analysis to inform instructional changes. Using Data currently works in districts and schools nationwide, building teacher-led data teams and facilitating a proven process of data analysis, instructional improvement, and increased student achievement—all leading to successfully narrowing achievement gaps among student population groups.   

The meeting was coordinated by WestEd and Education Northwest, and supported by the Bill and Melinda Gates Foundation. It brought together 50 nationally recognized experts who have studied the meaningful use of education data to improve instruction. They represented several universities, education research organizations, professional development providers, and foundation leaders.

Diana shares a glimpse of the discussions that ensued at the meeting and the musings they spurred. She concludes with a call to action for all who are committed to excellent education for all children… (more…)

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

“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” Albert Einsteinmagnifying glass trained on the word why in red text

Once a school or grade-level data team has analyzed several data sources to pinpoint a student learning problem, they often feel ready to leap into action and solve it. To ensure that the solution pursued produces the hope-for results, it’s essential to engage in a collaborative process of causal analysis to identify the “root” cause of the problem.

There are many tools that support root cause analysis, one of them is referred to as Why-Why-Why—a question-asking technique used to explore cause and effect relationships. Why-Why-Why helps a group look beyond symptoms to underlying causes by taking the identified problem and asking why it exists at least three times—each time probing more deeply. (more…)

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…)

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