The Diagnostic Process and Alignment

Warning: It’s dry and illuminating.

I have to take a course on data-driven instruction for my doctoral program in curriculum, instruction, and assessment. Y’all should know how I feel about data, but I’m actually learning a lot. More importantly, the landscape of my district is becoming more clear, particularly in terms of racial equity and ethnic studies.

For this course, I have to write a rather lengthy paper on data-driven decision-making. My instructor is having us chunk it out into smaller papers due each week. I’m going to post them here in a special series. I think I’ll call this “Special Edition PhD Series: The Devil in Seattle Public Schools’ racial equity data.” No? Maybe I’ll think of something more catchy, but probably not. I know it’s very Seattle specific, but I’m sure it’s not too different from other districts. Hopefully it sparks a conversation.

District Profile

            Seattle Public Schools is the largest district in the State of Washington. As of October 2018, the district consists of 52,931 students, 102 schools, and 4,519 educators. There are 61 K-5 schools, 11 K-8, 12 6-8, 12 9-12, and 16 self-contained schools. Eighty-two percent of students graduate high school on time. Thirty-one percent of students are enrolled in free and reduced meal programs. Twenty-one percent of students come from a non-English speaking background, and 147 dialects/languages are spoken by students (Seattle Public Schools, n.d.).

Seattle Public School’s mission statement is, “Seattle Public Schools is committed to ensuring equitable access, eliminating the opportunity gaps, and excellence in education for every student,” and the vision statement is, “Every Seattle Public Schools’ student receives a high-quality, world-class education and graduates prepared for college, career, and community (Seattle Public Schools, n.d.).” While the district boasts that, “Our district outperforms the state’s academic average and often perform better than similar district’s nationwide (Seattle Public Schools, n.d.),” the district fails to mention that Seattle Public Schools has one of the largest gaps in discipline and achievement between white students and students of color in the nation, and has been under investigation by the Federal Department of Education because of egregious disparities in discipline rates among racial lines (Dornfield, A., 2017; Shaw, L., 2014).

The disaggregated demographic data from Seattle Public Schools tell us that 48.5% of students are female and 51.5% are male. Students of color are the majority with a 41.8% white student population. One half of a percent of students are Native American, 14.1% are Asian, 14.9% are Black/African American, 12.1% are Hispanic/Latinx, 0.5% are Asian Pacific Islander/Native Hawaiian, and 10.8% identify as two or more races. Fifteen and one tenth percent of students are enrolled in special education and 3.4% have a 504 plan (OSPI, 2018).

Despite the vision statement of closing gaps and providing excellence for every student, disparities remain consistent. White female students graduate at the highest rates while Native American and Latinx males graduate at the lowest rates and are pushed out (dropout) at the highest rates (Seattle Public Schools, n.d.). Black and Native American males are suspended at 6 times the rate of their white peers while attendance rates for Asian Pacific Islander/Native Hawaiian and Native American females are close to ten points lower than their white peers (Seattle Public Schools, n.d.).

Standardized test scores continue to underline these racial disparities in achievement. The district website only provides standardized test score outcomes up to the 8th grade because there is such a high incidence of opt-out rates in high school that the data become inconsistent (OSPI, 2018). In 2017, 60% of all students met standard on the “Smarter Balance” standardized test for English language arts. Forty-nine percent met standard for math. When the disaggregated data is analyzed, however, the data is much less favorable. Thirty-three and nine-tenths percent of Native American students meet standard in English language arts and 24.9% for math. Seventy-nine and two-tenths percent of Asian students meet standard in English language arts and 74.5% for math. Thirty-five and three-tenths percent of Asian Pacific Islander/Native Hawaiian students meet standard for English language arts and 22.6% for math. Forty and five-tenths percent of Black/African American students meet standard for English language arts and 27.5% for math. Forty-two percent of Hispanic/Latinx students meet standard for English language arts and 30.7% for math. Sixty-seven percent of white students meet standard for English language arts and 55.4% for math. Sixty-two percent of students who identify as two or more races meet standard for English language arts and 49.6% for math (OSPI, 2018).

The district recently created a new strategic plan. While this plan identifies the types of data to be collected to measure success, there is no indication of a plan for analyzing or sharing data (Seattle Public Schools, n.d.). The district has identified a set of strategies to help reach the goals outlined in the strategic plan. The district has a SMART goal it has identified as priority 1, Eliminating Opportunity Gaps, which includes the following strategies:

  • CSIP’s Equity Goals / Building Leadership Teams (BLT) training
  • Ethnic Studies Planning and Pilot
  • Race and Equity Teams (RET)
  • Preventative and Positive Discipline
  • Family Engagement/Partnership
  • My Brother’s Keeper (MBK)

The district does not, however, identify any strategies to monitor the efficacy of these programs, nor are there any data immediately available to determine their current impacts (Seattle Public Schools, n.d.).

Accountability traditionally has been a site-based model in which individual principals and building administrative teams have determined which data to focus on and how to hold educators accountable. The district does have a multi-tiered system of support (MTSS) that acts as a form of accountability, particularly for those schools that are struggling with high rates of discipline and low rates of achievement. The MTSS team consists of special education educators, English language learner educators, school counselors, and content coaches for math and literacy. There is no racial equity component of the MTSS team or interventions.

The strategic plan calls for the collection of the following data, again, without any advice on how to analyze data or measure success:

  • Academic performance in early literacy and math for students of color who are furthest from educational justice
  • On-time graduation and college and career readiness for students of color who are furthest from educational justice
  • Social-emotional learning and welcoming school environments for students of color who are furthest from educational justice
  • Families are well-informed regarding district services
  • Improve operational performance in support of student learning
  • Improve diversity of staff and leadership at school and central office
  • Improve cultural competency and responsiveness of educators
  • Improve the environment for employees of color
  • Increase voice and leadership in school and district initiatives for students of color who are furthest from educational justice
  • Improve engagement around school and district initiatives with families and communities who represent students of color who are furthest from educational justice (Seattle Public Schools, n.d.).

There is no public or visible evidence of any system that gives educators access to district-level decision making, goal setting, or planning. There are partnerships between the district and the local bargaining unit, but these partnerships rarely involve classroom educators, and meetings and discussions generally consist of district and union leadership.

Data Analysis

            The data available to analyze all relate to instruction because they inform about who is achieving at various levels, who is engaged in instruction, who is delivering instruction, and how instruction is being delivered. They cover a variety of inputs and outputs including demographics of students and educators, graduation and push out rates, attendance rate, and discipline rates. Each of these data can be measured at classroom, school, and district level. Only testing data is disaggregated by grade level, and it is only disaggregated by content area as it pertains to “tested subjects.” No data exists for achievement in social studies, arts, language, physical education, or other content areas.

While all data can be disaggregated by race and ethnicity, not all the data collected by the district is disaggregated, specifically student climate data and educator data. The district reports on the race and ethnicity of teachers but does not collect disaggregated data on teacher attrition.

A longitudinal study of data would be relevant in meeting the district’s goals of closing achievement gaps, but there seems to be a lack of such data. An analysis of instruction, in particular, would require longitudinal data. Longitudinal data would inform educators how changes in instruction, or lack thereof, have contributed to existing gaps. Only through longitudinal data can themes, patterns, and inconsistencies be discovered. Starting with data about existing gaps is not a systems approach to data analysis (Bernhardt, V.L., 2016).

Data as tools to measure growth need to be formative and summative. Formative data are attendance rates, student climate surveys, historic data, and educator data. Summative data are data on graduation rates and test scores. The formative data are a measure of where we have been and where we are, and the summative data are measures of where we would like to be.


The district’s mission and vision statements and strategic plan are very clearly crafted around the needs of students of color and the disparities in achievement that exist between white students and students of color. The district has a wide range of data available that indicate a need for their stated mission and vision statements and strategic plan.

The districts goals and strategies are consistent with racial equity in instruction and outcomes, but there are gaps in the ways in which they intend to measure the success of these strategies. Despite the wealth of evidence about the inherent racism and ineffective practice of standardized testing (Au, W., 2008), most data used to measure “achievement” are related to standardized testing outcomes, and while most data are disaggregated by race and ethnicity, no data seems to be collected about race and ethnicity. Collecting disaggregated data on teacher attrition, the racial equity literacy level of educators and administrators, and student climate survey disaggregated by race will paint a clearer picture about how educators and students of color feel about instruction and learning in Seattle Public Schools. By only collecting racially disaggregated data on outcomes, education becomes “about” students of color instead of “for” students of color.



Data Analysis Worksheet

Name of District:                Seattle Public Schools                                                                

Diagnostic Purpose: To improve instruction

Data that are available What do the data tell us? To what extent do they inform us about instruction?
Demographic data Who the students are Alone, they do not. When disaggregated they tell us which students instruction is effective for and which it is not.
Test score data Who is “meeting standard” These data tell us exactly how racist testing is and how we should not align instruction with testing data.
Graduation rates Who is graduating. If instruction is not engaging all students, some will leave school before graduating.
Push out rates Who is not graduating See above
Attendance rates Who is engaged in learning If instruction is not engaging for all students, some students will not make school a priority.
Educator data Who is teaching How well educators are relating to the lived experiences of their students and how that is seen in instruction practices and content
Free and reduced meal data Income levels of families How well educators are differentiating instruction to meet the socio-emotional needs of students
Multilingual data How many students speak more than one language How well educators are differentiating instruction to value the strengths of multilingual students.
ELL data How many students are learning English See above
Special education data How many students need extra services and supports How well educators are differentiating instruction to meet the needs and provide supports
Discipline data How many students are being pushed out and who those students are How many instruction hours are being missed and which group of students are missing the most


Data that are not available: Gaps in the data What could the data tell us about? To what extent would they inform us about instruction?
Racial equity literacy of educators and administrators How well educators are prepared to meet the needs of currently underserved students How racially biased instruction practice and content are
Student climate survey data disaggregated by race Which students feel safe and welcomed in their school environment How well instruction is meeting the needs of currently underserved students
Historic data about instruction and discipline How the gaps were created These data would tell us if instruction has been changed to close gaps or if instruction has remained unchanged which perpetuates gaps
Disaggregated teacher attrition rates What the district is doing to retain educators of color Teachers of color have  a tendency to be more culturally responsive in terms of instruction.


Data that are available Data that are not currently available
Less Important Must-Have Less Important Must-Have
Test score data Demographic data   Racial equity literacy of educators and administrators
Free and reduced meal data Graduation rates   Student climate survey data disaggregated by race
Multilingual data Push out rates   Historic data about instruction and discipline
ELL data Attendance rates   Disaggregated teacher attrition rates
Special education data Educator data    
  Discipline data    


List the data you want to include in this district’s Data Collage/Data Profile.
Element of Data Collage/Data Profile Describe what these data contribute to the Data Collage/Data Profile
Demographic data Without this data we cannot accurately define the gaps and target the groups of students who need the most support.
Push out rates This will tell us who is the least engaged in the education process.
Attendance rates This will tell us who is the least engaged in the day-to-day learning.
Educator data How well educators are relating to the lived experiences of their students and how that is seen in instruction practices and content – who do we have and who do we need?
Racial equity literacy of educators and administrators These data will measure the degree to which the district prioritizes racial justice.
Student climate survey data disaggregated by race These data will give us insights into the experiences of students in school based on their racial and ethnic identities.
Historic data about instruction These data would tell us if instruction has been changed to close gaps or if instruction has remained unchanged which perpetuates gaps.


The data profile I am creating consists of the data I believe is the most relevant to the district’s mission statement, vision, and strategic plan. All three involve closing gaps between white students and students of color. These disparities have existed since I began working with this district in 2013 with some disparities increasing in the past year despite a recent push for racial justice. If we are going to address racial disparities, the focus on data should highlight the racial and ethnic backgrounds of students, educators, and outcomes.

The process I used in selecting these data was researching the data that are readily available on the Seattle Public School’s website and the Washington State Office of the Superintendent of Public Instruction’s website. I used this worksheet to help me find the data that are available versus the data that are not available or are not collected. In some instances, the data are available but not disaggregated by race or ethnicity. I then went through the list of available and unavailable data and prioritized them using the district vision and mission statements and the strategic plan as guides.

Demographic data are important because we are focusing on specific groups of students in our district goals. All data, especially demographic data should be disaggregated by race and ethnicity in order to ensure we are analyzing the correct data to meet our goals. I chose to include push out rates instead of graduation rates, because graduation rates tell us how well we are doing with instruction and push out rates will help us determine whose needs we are not meeting. While push out rates are a summative assessment of engagement, attendance rates are a formative assessment. Student climate data are often left out of this discussion. They are often used in building level discussions on how teachers can improve practice and instruction, but I have not seen them as part of the district level planning. I have left out testing data because of their inherent racism (Au, W., 2008).

The last few data sets are about educators instead of students. I believe that focusing solely on students is deficit thinking. We do not need to fix the kids. We need to fix the system that is failing the kids. Since the district claims it is committed to racial justice in schools, key data that are missing is how literate educators and administrators are in racial equity. Racial justice initiatives are only as effective as the people creating and implementing them, and our district has neither mandated district-wide training on racial equity nor does it have a way to assess the levels of racial equity literacy in its staff. It is also important to know the racial and ethnic backgrounds of educators since studies show student engagement and success is linked to the racial and ethnic background of their teachers (Anderson, M., 2015).

Historic data about instruction is important because it seems that the district strategic plan is beginning and ending with achievement gaps. In her book, Data, Data Everywhere; Bringing all the data together for continuous school improvement, Dr. Bernhardt emphasizes the need for data to show an organization where it has been, where it is, and where it needs to go (2016). The district’s strategic plan skips the first layer of data analysis. This is particularly important in terms of instruction and assessment, because we tend to do more of the same in hopes the outcomes will change. Data on previous instruction and assessment is needed to understand how gaps are created. Data on the types of instruction and assessment are needed, not only the outcomes of each.



Anderson, Melinda. (06.08.2015). Why Schools Need More Teachers of Color – for White Students; Nonwhite educators can offer new and valuable perspectives for children of all backgrounds. The Atlantic. Retrieved from

Au, Wayne. (2008). Unequal by Design: High-Stakes Testing and the Standardization of Inequality. New York, NY: Routledge.

Bernhardt, V.L. (2016). Data, Data Everywhere; Bringing all the data together for continuous school improvement. New York, NY: Routledge Taylor and Francis Group.

Dornfield, A. (06.08.2017). Fewer Seattle students are getting suspended, expelled, data show. KUOW. Retrieved from

OSPI. (2018). Seattle Public Schools [Data set]. Washington State Report Card. Retrieved from

Seattle Public Schools. (n.d.). About Seattle Public Schools. Retrieved from

Shaw, L. (27.03.2014). A year later: What’s up with school discipline case in Seattle? The Seattle Times. Retrieved from