Keep Data Inspiring! What Inspires You?

On a recent hot walk to build up my endurance for the Maine Half-Marathon in October, I heard an interview on The New Yorker Radio Hour with a musician who was new to me, Regina Spektor. When asked by the host, about how and where she composes her music, Regina’s way of working rang my bells personally and professionally. Regina described how artist friends of hers have a daily routine with set times when they enter their studio or writing nook and lay down the notes or words leading to a new composition.  While she admires their discipline and what it leads to, she was quick to describe how her creativity isn’t regimented by the clock.  Her creations come from INSPIRATION, when something she observes, hears, or experiences, provides the germination of a new composition.  On a personal level, when it comes to writing, I’ve always envied those who set their alarms, and write.  Doesn’t work for me. I have to be inspired and then I grab my phone to enter quick notes until I can get to my computer or iPad. Inspiration from whatever source always leads to threads going off in multiple directions as did the inspiration from this interview.

Our work for many years at the Using Data Project, now Using Data Solutions, has been to help teacher teams learn how to analyze student data both formal and informal in order to plan and adjust instructional strategies. A foundational cornerstone of Collaborative Inquiry relies on principals and department heads creating schedules that provide teacher teams regular time everyday / week to analyze results and plan instruction. Schools that invest in this practice begin to see the needle move upward as their students begin to “get it” at higher rates and with deeper understanding. This is the discipline that leads to growth gains, many of which come when teachers are inspired to try new strategies or fill gaps in the curriculum until they find the pieces that work. And yes, data can be inspiring.  It can reveal patterns or trends un-anticipated. Or a colleague’s comment during the team meeting, can inspire a new line of thinking. But this is a discipline.

Let’s consider the other part of this discipline.  Team meeting is a daily, weekly, sometimes bi-weekly scheduled working session. There’s preparation involved to collect and share the data to be analyzed. There is a routine protocol for taking the data discussion to new levels. And it works. We have years of evidence to support this discipline. But let’s not overlook the other sources of inspiration and how they too, shine a light on new pathways.  In a recent EdWeek article (What Teachers Love Most About Their Jobs and Why by Madeline Will, July 31, 2023), teachers noted where even in the difficult times they’ve just lived through, their joy comes from what happens in the classroom.  It’s seeing students’ aha moments – “I got this!”. it’s observing a student engaging and experiencing something they didn’t think was possible. It’s hearing students begging for one more chapter to be read, or a teacher constructing learning and seeing it evolve as students take hold of it. 

We never want the discipline and routine of PLCs or Data Team Meetings to be perceived as “just one more thing”, a drudgery to be endured.  Inspiration comes in many forms and data team meetings should include the informal, observational data that brought joy to a teacher and is just as valid as all of the interim assessment’s charts and graphs or student work showing student progress.  Data Team meetings should collect and chart the impacts of teachers’ intuition and experience that leads their instruction in a different direction. If the intuition was off center or the experience was missing an element, the data will be revealing, just as it will be when the teacher’s intuition was right on target.   Inspiration comes differently to us all.  Let’s recognize and value it however it comes. 

 

What’s Working?

As educators look at End-of-Year state assessments, End-of-Course results and other data points used to track progress toward achievement and growth goals, teachers are getting more experienced and comfortable identifying gaps in learning.  What they aren’t as comfortable and confident about is knowing what to do next – how to use what they’ve learned to take action beyond student grouping decisions – how to create lesson plans with specific instructional strategies aimed at engaging students more effectively in the concepts and skills needed. An even further reach is encountered when it comes to expanding their investigations into verifying the reasons “Why?” students are experiencing the challenges revealed. 

Continuous, effective use of data by teachers requires that all three components of learning from our data are part of professional routines in a culture where we believe it is our moral responsibility to help all students thrive.

1. Examine student learning and other relevant data to identify who is getting it, who isn’t, what aren’t they understanding and what aren’t they able to do successfully.
2. Use that information to plan specific instructional strategies to engage students in the areas needing additional learning and plan how to measure the impact of those strategies.
3. Continue to broaden the analysis to identify and verify the root causes of student learning gaps.

No. 2 above is perhaps the most critical component of the cycle and it actually speaks to a topic that

in itself warrants careful attention and requires additional analyses beyond the classroom. What’s working? And what isn’t? Which students are we leaving behind?

The reality in most schools is that as teachers are focused on their students, they are also often implementing new materials, new intervention programs (Social Emotional, Growth Mindset, Brain-based Learning, etc), developing higher level questioning strategies, identifying ways to scaffold rigorous instruction for ALL students. The list goes on and it’s all happening concurrently. But when we see improvements or our state report card ranking moves up a level, we aren’t sure which programs or combinations of changes contributed to the outcomes, or which ones had no impact whatsoever, except on our budget.

New programs and initiatives should from the outset include the following questions: What is the change we want to see, what will it look like when it’s implemented successfully, how will we know that it is successful, what is the evidence we’ll gather to help us know if it’s working?

Are you asking these questions?

 

Who Gets Results? Busy People and Wise Leaders Part 2: Supporting Your Data Leaders

Guest Blogger: Jennifer Unger

In Part I*, I offered an insight to educational administrators about the merits of leaning on your busy people—those already involved in other school and district improvement efforts—as your data leaders. In Part 2, I share a few thoughts about the level of support a wise leader provides to ensure that these people are successful.

It’s spring, and a good time to take stock of how using data has informed practice and affected student achievement at your site since the school year began.

Earlier in the year, did you make the decision to integrate broadly-implemented data tools and processes into the assessment/evaluation plan for your school or district? If you have not yet formalized a using-data effort, should you?

As mentioned in Part 1, the first step is identifying the Data Coach and data team members. Some schools refer to these people as their improvement team or teams. Once in place, reflect on the level of support and direction you need to provide. Here are some possible questions and ideas to consider:

 

Who Gets Results? Busy People and Wise Leaders Part 1: Selecting Your Data Leaders

 Guest Blogger: Jennifer Unger

There’s a familiar saying, “If you want something done, give it to a busy person.” If comments I hear when working with educational leaders can be taken as evidence, then it’s true.

In order to use data for meaningful change, TERC’s Using Data project advocates the identification of a Data Coach to lead a Data Team or multiple Data Teams. When we talk with school leaders about who might best serve as a Data Coach or team member, I hear comments such as, “I really think Dana would be a great data leader (or team member), but she/he is already involved in so many initiatives.”

In my experience, wise leadership makes all the difference. Let’s explore this dilemma more deeply…

 

The Journey to Create a Data Driven School Culture

Collaborative inquiry posters showing causal analysis based on San Mateo Elementary School data

The Data-Aware Principal: Reflection #1
Guest Blogger: Lindsay P. Sharp, Principal, San Mateo Elementary School, Duval County Public Schools, Jacksonville, FL

As a principal, it’s clear to me that I need to be data informed. My job depends on it—literally, since I am evaluated by my school’s achievement. More importantly, though, my heart depends on it—I am committed to seeing data not as just numbers, but connected to the success of the students and teachers in my school.

As the school’s leader, my thoughts turn to the best way to translate my own state of “data informed-ness” into meaningful action, and I have come to understand the key lies in putting my efforts into creating data leaders beyond the principal’s office. My Using Data colleagues are now in every classroom in my school! Accomplishing this level of a “using data school culture” depends on a process that involves professional development, support, and dedication over time. We work at it every day.

 

Using Data Tip #9: Disaggregating Data Makes the Invisible Visible

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. 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?

 

Using Data Tip #8: Triangulate, Triangulate, Triangulate

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

All too often state test results may be the only source consulted when targeting specific areas for improvement. However, decisions about instructional changes that reflect only this single data source, might lead to errors in your decision-making.

If you want your data to lead you toward making meaningful changes, an important principle to follow, is triangulation. Triangulation means using three independent data sources to examine apparent issues or problems. You might ask, “Why bother with the extra work of triangulating?” Consider this analogy:

A third-grade teacher asks Mary to look through the front panel of the classroom terrarium and list everything she sees. Mary diligently makes a thorough list and begins to return to her seat when the teacher asks her to take a second look through the side panel of the terrarium. She immediately sees several plants and animals obscured in the front panel view by rocks and shrubs. By using this second “window,” Mary now has a more complete picture. Then the teacher asks Mary to peer through the top of the terrarium to see if there is anything else. Mary is able to add to her list before she sits down. Her three-window analysis reveals a far more comprehensive picture than any one window alone.*

The notion of using multiple windows or perspectives also applies to understanding and applying information from student achievement data. Consider these Action Steps:

 

Using Data Tip #7: Finding the Time For Data Inquiry

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

As powerful as an inquiry process might be, it is only good if practiced regularly.

Recently, we met with teams of teachers in Florida who are learning the TERC Using Data process of school-based collaborative inquiry. Between our two scheduled face-to-face sessions, these data teams returned to their schools to apply the process they had learned and dig deeper into their own data analysis with colleagues. One returned with an epiphany. “I thought we were learning a quick way to ‘fix’ things. I now realize that there is no quick way to do this. You just have to take the time to engage in the process, understand what to do to get results, and do it!”

Meaningful data analysis, pinpointing student learning problems by triangulating multiple data sources, deconstructing student work samples, finding root causes for emerging problems, and launching a plan to tackle these problems takes time.

Anyone who has ever integrated inquiry into classroom instruction knows how time-consuming it is…and how valuable. The same holds true for a data analysis process based on collaborative inquiry.

 

Using Data Tip #6: Beginning a Causal Analysis Process: Why Asking “Why” Once is Not Enough

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 Einstein

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.

 

Using Data Tip #5: Getting to the Bottom of “Why” Before Investing in Solutions — Make Inferences and Question Your Data’s Story

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.)

Data 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.