The Data Coach's Guide to Improving Learning for All Students. Katherine E. Stiles
them together.
In addition, Data Coaches do more than facilitate data use and dialogue; they play a role in influencing school culture and strengthening relationships of trust and shared commitments. Data Teams build a foundation for a strong collaborative culture in the early tasks by anchoring the Data Team’s work in shared values, vision, and a goal of creating a high-performing, data-using school culture. The Data Coach guides the team to keep the values and vision alive and to periodically assess the team’s progress in achieving their larger goal of creating a data culture. In addition, the Data Team and Coach continually strategize about how to reach out to other initiatives in the school and/or district, engage the faculty and community in Data-Driven Dialogue, and broaden the impact of the Data Team on the entire school.
Long-Term and Short-Cycle Improvement
The Using Data Process accommodates both long- and short-cycle improvements. The 19 tasks can unfold over a 1- to 1½-year implementation cycle and can operate as the school’s ongoing school improvement system. The team goes through a rigorous process of examining multiple data sources, identifies a problem, uses other data sources to verify the causes of the problem, and builds a logic model to link school improvement strategies to desired outcomes. They carefully plan implementation and monitor results. They think systemically and develop plans to improve several aspects of their system, such as their policies, grouping practices, and instructional materials.
But a Data Team can also move much more quickly through the Using Data Process, even in a week or a month. For example, the Data Team examines quarterly benchmark assessment data one day and discovers a student-learning problem related to specific content. They immediately generate possible solutions, which are tested out in the classroom that week, gathering data to assess whether the solution improved student understanding of the specific content. Within the longer-term improvement process, the Data Team is continually improving instruction by regularly monitoring student learning, implementing new practices, and collecting evidence of effectiveness. In this way, the Using Data Process combines the rigor of a long-term improvement process with the immediacy of short-cycle changes.
Look Before You Leap
During our work with schools, we observed Data Teams that were quite proficient at using data to identify problems, but, initially, they did not do the best job of verifying the causes. Often they would leap to assigning cause without checking out their theories. For example, as you can read in the Clark County, Nevada, case study in Chapter 8, one of the Data Teams accurately identified that their students had difficulty solving nonroutine mathematics problems. They concluded that the cause was that students were not persisting and that they were unable to read the problems. It wasn’t until they actually observed 40 students engaged in problem solving that they saw their theory didn’t hold water. Students were persisting. And even when the problem was read aloud to the students, they still weren’t able to solve it. Through their observations and by consulting research, the team came to the conclusion that the cause of the problem was that students lacked problem-solving strategies, not persistence. This led to a very different course of action. The Using Data Process builds in checks and balances to guide teams to verify the causes of problems with data and research. Another place the process can break down is in generating solutions. Data Teams would seize upon a strategy such as implementing a professional development workshop. But they wouldn’t think through exactly what that workshop would accomplish that would get them closer to their student-learning goal. This led us to incorporate logic-model thinking to increase the accuracy of Data Teams’ action planning.
Student Learning Improves in Schools Implementing the Using Data Process
When our middle school mathematics Data Team received their most recent state achievement test results, they broke into cheers and tears. That’s ownership!
—Pam Bernabei-Rorrer, Mathematics and Data Coach, Canton City, Ohio
The true power of collaborative inquiry is its potential to improve student learning. In Canton City, Ohio, all four middle schools, comprising 66–82 percent poor students and 30–45 percent African American students, increased the percentage of students scoring proficient or above on the Ohio Proficiency Test in mathematics between 2002–03 and 2004–05. One school doubled the percentage while another more than doubled it. The percentage of African American students passing the Sixth Grade Ohio Proficiency Test in mathematics almost tripled from 2002–03 to 2004–05 (Ohio Department of Education, 2005). (For more details on these and other results, see Handout H1.3 on the CD-ROM for Task 1.)
On the Ohio Seventh and Eighth Grade Achievement Tests, all student groups, including all racial groups, students with special needs, those receiving free and reduced lunch, and males and females, made gains. The percentage of high school students earning proficient or above on the Ohio Graduation Test increased by 25 percentage points from 2004 to 2006. As in the seventh and eighth grade, all student groups made progress (Ohio Department of Education, 2006).
In Clark County School District, Nevada, the Wendell Williams Elementary School (with 100 percent of students eligible for free and reduced lunch, 75 percent African American and 19 percent Hispanic) saw improved student performance in third- and fifth-grade reading and mathematics on the Nevada criterion-referenced test (CRT) in one year. The percentage of students scoring proficient or above in fifth-grade mathematics jumped from 18 percent to 42 percent (Nevada Department of Education, 2005). Similar results were achieved in other participating schools.
In Johnson County, Tennessee, a poor, rural area with 70 percent of students on free or reduced lunch, the schools exceeded the growth rates of some of the wealthiest and highest-performing districts on the state assessment. Most impressive were gains for students with disabilities. In Grades 3, 5, and 8 mathematics, the percentage of students with disabilities proficient in mathematics increased from 36 to 74 percent from 2004 to 2006. In reading for the same grade levels, the percentage proficient increased from 54 to 70 percent (Tennessee Department of Education, 2005, 2006).
Several of the schools participating in the Arizona Rural Systemic Initiative in Mesa, Arizona, serving a high percentage of Native American children, made gains in student achievement on the Arizona State Assessment. For example, San Carlos Junior High School in San Carlos, Arizona, cut the percentage of students in the Falls Far Below category from 95 percent in 2001 to 45 percent in 2005 in eighth-grade mathematics and met Adequate Yearly Progress (Arizona Department of Education, 2005; Eileen Armelin, personal communication, September 29, 2005).
Schools Build High-Performing Using Data Cultures
Equally exciting are changes in school culture, instructional improvement, data use, collaboration, and leadership. The evaluation provided evidence that the shifts toward high-performing Using Data cultures are taking hold in schools that participated in the Using Data Project. “As a result of UDP participation, many teachers have reported a significant shift in their culture of using external factors to explain lack of student achievement. Many acknowledged that the process of discussing student test data has made them more accountable for the results and more mindful that teachers are in a position to influence gains in student outcome” (Zuman, 2006, p. 2).
I don’t think we can ever go back. Using Data has become a part of our culture.
—Mary Ann Wood, Data Coach, Salt River Elementary School, Mesa, Arizona
The next chapters will walk you through the steps you can take to unleash the power of collaborative inquiry.
2 Getting Organized for Collaborative Inquiry
This chapter discusses how school districts and individual schools lay the groundwork for successful implementation of the Using Data Process. It answers basic questions about what to consider as you get organized for implementation and how to get started: How can collaborative inquiry be integrated into