Data driven decision making

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.

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…)

By Diana Nunnaley, Director, TERC’s Using Data

March Madness annually takes over the country, or at least the media and the minds of U.S. college basketball fans who give itFather and son playing basketball their frenzied attention each spring. At the same time, another March Madness is going on that does not garner the same enthusiasm and  does not make national news in quite the same way. It’s the March Madness going on in schools across the country as teachers and administrators ready for spring, state-initiated student accountability assessments. These tests are considered by some to definitively provide feedback on how much students have learned this year, and correspondingly – how effective their teachers are. (That second-tier “madness” could fill volumes, and I chose to let the pundits continue to hash out that one.) (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: Dr. William L. Heller, Using Data Program Director, Teaching Matters*

There are often revelatory moments in the data inquiry process, where your analysis will lead to great insight and discovery in a way that challenges your assumptions and changes the way you think about teaching and learning in your school. There are other times when the data shows exactly what you werePen pointing to detail of bar graph showing flat results expecting, confirming your predictions and giving you valuable evidence in making your case to others. Many times, however, the data doesn’t show anything at all.

This can be somewhat dispiriting to an enthusiastic data team, but it doesn’t need to be. Sometimes the data may show nothing, but that’s still valuable information that puts you ahead of where you were before you looked. We don’t complain when our dentist finds no cavities, when the mechanic finds nothing wrong with our car, or when a medical test comes back negative. Similarly, in data inquiry, even a finding of nothing can really be something, if you know how to interpret what it means. (more…)

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