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Pages Answering the Questions That Count David Ronka, Mary Ann Lachat, Rachel Slaughter and Julie Meltzer Examining student data through the lens of pressing questions can mobilize staff, promote data literacy, and help raise student achievement. Daily life in districts and schools requires educators to effectively navigate a sea of data: This new level of applied data use requires district and school administrators, teacher leaders, and classroom teachers to be data literate, that is, able to use multiple types of assessment and other data to inform decisions that lead to higher student achievement.
Despite the increased amounts of data available, many educators still feel ill prepared to analyze and use their school data effectively. They are data rich, but information poor.
Our experiences working with data use in schools and districts have led us to define an effective framework for building data literacy.
This framework is fueled by an essential-questions approach that organizes data use around a cycle of inquiry and is grounded in three core components of systemwide data use: Organizing data use around essential questions about student performance is a powerful strategy for building data literacy. Consider the following questions: How do student outcomes differ by demographics, programs, and schools?
To what extent have specific programs, interventions, and services improved outcomes? What is the longitudinal progress of a specific cohort of students? What are the characteristics of students who achieve proficiency and of those who do not? Where are we making the most progress in closing achievement gaps?
How do absence and mobility affect assessment results? How do student grades correlate with state assessment results and other measures? Asking questions such as these enables administrators and teachers to focus on what is most important, identify the data they need to address their questions, and use the questions as a lens for data analysis and interpretation.
To avoid the common tendency to get lost in a long list of questions, district or school staff should, in general, identify no more than five or six crucial questions that get at the heart of what they need to know.
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The essential-questions approach provides the fuel that drives collaborative analysis. But to use data purposefully and in a sustained way over time, schools and districts have to address three interrelated components of systemwide data use. Data Quality Teachers and administrators need to believe in the completeness and accuracy of the data they are expected to use. Data must be sufficiently disaggregated to address questions of concern, displayed in easy-to-understand formats, and available in a timely manner for instructional planning.
The most effective way to ensure these conditions is to use technology that supports data disaggregation, provides data access, and generates useful data displays.
In addition, disaggregated data linking attendance, mobility, or course grades to assessment results are helpful when looking at the kinds of factors that may influence student performance. Although many schools we have worked with have some form of data-warehousing technology, training typically focuses on how to use the technology itself rather than on how to make meaning of the data.
The essential-questions approach has helped educators recognize the power and potential of going beyond aggregated data to identify the data they need and when they need it and to define multiple ways of disaggregating the data. Hispanic students—many of whom were English language learners—constituted more than one-half of the population in all three high schools; the percentage of low-income students ranged from 60 to 85 percent.
Teachers developed the following questions: How did students in each of my 10th grade course sections perform on the 9th grade reading vocabulary and reading comprehension assessment?
Do some of my course sections have a higher proportion of students below grade level in reading skills? How can I meet the instructional needs of students with varying skill levels in reading?
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Attempts to answer the questions revealed several data-quality issues: The schools had received aggregate total reading results rather than subskill results. The high schools had no test results for students who had transferred from another high school in the district.
The schools did not have a dissemination plan to get data to teachers on a timely schedule. With the assistance of the data coach, school principals developed a dissemination plan that identified what data would be available and when, who would get the data, and how staff members might use it.
Addressing data quality and disaggregating data for different course sections meant that the 10th grade teachers could answer their essential questions about students currently in their classes. They learned that most students were below grade level in recognizing synonyms and determining meaning for words with multiple meanings but were at grade level or above in using context clues.
This enabled teachers to target instruction to focus on word study and word analysis. How much instructional support will teachers need for students below grade level in reading vocabulary and reading comprehension?
These included strategies for building vocabulary, helping students understand text structures, and using anticipation guides, graphic organizers, and think alouds.
The focus on data continued beyond the reform initiative, and the disaggregated subskill reports became part of the core set of reports the district provided to these high schools. The high school reform initiative also involved using data-warehousing technology to follow the progress of three cohorts of students from grade 9 to grade 11 on one particular subskill of the state assessment.
All three cohorts showed some increases in the percentage of students achieving proficiency in this subskill and significant decreases in the percentage of students scoring at the lowest level of the assessment. Effective data use will not occur unless schools also address data capacity. At the district level, representation should include leadership in curriculum and instruction, the elementary and secondary levels, special programs, student personnel services, research and assessment, and student information services.
School teams should include the principal and other instructional leaders, personnel from the guidance department, and grade-level or subject-area teacher representatives. Schedule time for collaborative analysis at key data points, such as when pertinent assessment or quarterly data on attendance and course grade patterns become available. This enables schools to define an annual schedule of when data teams will do their analysis and improvement planning.
What It Looks Like in Practice Many of the schools and districts we have worked with want to go beyond trend data in their analysis of student progress. One urban district in the northeastern United States specifically focused on building the skills of both district and school data teams to analyze multiple types of assessment data. Their analysis of the data led the teams to define questions about the progress of cohorts of students as they moved from one grade to the next.
The team particularly wanted to focus on whether early elementary students were improving their proficiency levels in vocabulary as measured by the reading assessment that all the elementary schools used. The team explored the data through the lens of the following essential questions: Are our students making sufficient grade-to-grade progress in vocabulary development? What are the characteristics of students who made progress and of those who did not? Participants discussed how to visually represent the data in displays that would facilitate analysis.
We have found that graphic displays created to address one or more essential questions are the most useful. The display shown in Figure 1, for example, enables viewers to examine the grade 3 performance in vocabulary of students who scored at each of three performance levels in that skill in grade 2. The district leadership established and trained district- and school-level data teams, allotted time to engage in collaborative analysis, and made available meaningful data displays driven by essential questions.
Ultimately, educators in this district were able to go beyond the superficial and often inaccurate conclusions of trend analysis and identify the specific effects of current programming. This enabled the district to target areas in which additional resources were needed to improve instruction.
Data Culture Achieving purposeful and sustained data use necessitates a culture shift. This entails establishing and providing leadership direction to data teams, modeling effective data use, scheduling time for collaborative data-driven conversations, and connecting data analysis to clear action steps.
Holcomb compellingly wrote about the importance of mobilizing broad stakeholder involvement and getting people excited about data use. What It Looks Like in Practice A small rural district had the following questions about the literacy habits and skills of its middle and high school students: How do the reading levels of our students compare with those of students across the state? How many of our middle and high school students read below grade level?
What are we doing to support accelerated growth in reading for students below grade level? The data-collection process, which occurred over the course of a few months, brought together student performance data, teacher survey data, and data on school capacity to support literacy.
When juxtaposed with information about school capacity, the data showed that neither the middle school nor the high school had an effective way to address the needs of the sizeable number of students who read below grade level. For example, teachers said that many students were unable to analyze what they read, did not like to write, responded to questions with incomplete answers, and had difficulty learning vocabulary. The majority of teachers reported that they did not use several instructional strategies that might address these issues, such as those relating to student choice, student inquiry, the use of technology and varied texts, and student discussion of text materials and what they have learned.
Recommendations included having teachers learn some common instructional strategies targeted to vocabulary development; motivating students to read and write in the content-area classroom through the use of collaborative routines, such as reciprocal teaching and paired reading and summarizing; and teaching students how to think critically when reading and writing.
We also recommended that teachers learn a common protocol for looking at student work and that the middle and high school each form a literacy team to support implementation of the literacy improvement initiative.
Other issues emerged during the data-collection process.
For example, teachers at the middle school level used three different reading assessments and lacked common protocols for testing. This made it difficult to track student progress. The high school had no system in place to determine the reading proficiency of incoming 9th graders. Despite the clear need, no interventions were available for struggling readers in grades 6—8, and few teachers or administrators in the middle and high schools regularly used existing data about student performance for placement, instructional decision making, or progress monitoring.
In response to our recommendations, the district took several steps that deepened data use at both the district and school levels. The district researched and selected a reading assessment for grades 6—10 that provided subskill reading performance data, including information about vocabulary and nonfiction reading comprehension. This enabled the district to monitor the progress of student cohorts, including those enrolled in intervention classes.
At the middle school level, teachers learned protocols for looking at student work and met with students to set reading progress goals. School leaders communicated the expectation that teachers would use the new reading assessment data to determine what types of literacy support needed to occur in content-area classes.
The data-based recommendations led to targeted professional development for the faculty. In the first year, professional development focused on vocabulary-development strategies, instructional strategies to promote engagement and critical thinking in reading and writing, and a common set of instructional strategies to improve reading comprehension. Teachers in some departments began an in-depth look at the literacy demands of their content areas and started to develop common agreements about what they expected students to be able to do.
In the second year, professional development focused on how teachers might promote reading and writing for authentic reasons within and across content areas.
The principal and vice principal conducted literacy walkthroughs to determine the effectiveness of professional development, and teachers received feedback on what was happening in classrooms schoolwide. According to staff, this combination of approaches contributed to gains in student reading achievement at the 6th, 7th, and 8th grade levels for the following two years.
Using data, which teachers and administrators can access online, is now an integral part of the culture in this district. Data use determines professional development needs, intervention requirements, and resource allocation; it focuses discussions about teaching and learning, guides teacher instruction, and monitors progress. Most important, teachers and administrators have a shared belief about its value. The Data Difference Schools and districts of all sizes can use the essential-questions approach to become data-driven decision makers focused on improving student learning and achievement.
Properly used, data can make a difference in meeting the needs of every student and can be a powerful ally in stimulating positive change and improvement from the central office to the classroom. Data wise in action. Culture and processes affecting data use in school districts. Getting excited about data: