Using Data


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.

Resources

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 www.mnpscollaboration.org/about.html

 

*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.

By Mary Anne Mather, Managing Editor
TERC’s Using Data for Meaningful Change Blog

LogicModelDuvalTERC’s Using Data facilitators have been working for the past two years with 30 elementary schools in Duval County Florida. This is possible through funding from a U.S Department of Education, Institute of Education Sciences (IES) grant to study the efficacy of the Using Data for Meaningful Change processes.

As our time together winds down, the Duval schools are sharing stories about transformation in practice, focus, and student achievement. (more…)

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

If you want to tap one of the most powerful uses of data, disaggregate! Disaggregation means looking at how specific subgroups perform. Typically, formal student achievement data come “aggregated,” reported for the population as a whole—the whole state, school, grade level, or class. Disaggregating can bring to light critical problems and issues that might otherwise remain invisible.

For example, one district’s state test data indicated that eighth-grade math scores steadily improved over three years. When the data team disaggregated those data, they discovered that boys’ scores improved, while girls’ scores actually declined.different colored stick figures sorted into color-coordinated groups Another school noticed increased enrollment in their after-school science club. However, disaggregated data indicated that minority students, even those in more advanced classes, weren’t signing up. These are just some of the questions that disaggregated data can help answer:

• Is there an achievement gap among different demographic groups? Is it getting bigger or smaller?

• Are minority or female students enrolling in higher-level mathematics and science courses at the same rate as other students?

• Are poor or minority students over-represented in special education or under-represented in gifted and talented programs? (more…)

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

I very much enjoyed Part I of Jill Thompson’s blog series about “Using Data to Drive Instruction in the Classroom.” According to her bio, Jill is an elementary math and science facilitator.

I applaud her for sharing her insights and passions about this subject. As a former classroom teacher, and currently as a facilitator for TERC’s Using Data process, I find myself in step with her thinking. Regularly integrating formal and informal assessments into the instructional planning process is a must. It’s not adding more to the plate — it IS the plate…understanding the impact of the teaching process on student learning and using that information to plan the necessary next steps—not only what to teach, but how to engage kids in the learning.

These days there is so much negative emphasis on testing, and I understand the rub when I see test scores being used to punish teachers and categorize kids. But let’s be clear that using data and testing are not the same thing. Data comes in many shapes and forms, well beyond test results and grades (these are just one data point). Teachers have the opportunity to use data as a valuable resource to guide a teaching and learning approach that can ignite learning for all students. As Jill notes–it just takes time and know-how (and an understanding that it’s a non-negotiable).

I plan to follow Jill’s blog series on this topic, and I recommend it to you. Thank you, Jill, for sharing your experiences and helping those who might be uncertain about how to put their data to work as an instructional tool. Your ideas illuminate understanding of a process for using data that can profoundly impact student engagement and achievement.

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…)

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…)

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