In this segment of my paper on data-driven instruction, I was asked to do an analysis of how data is used in decision-making and reflection to support standards-based learning across the district. I was asked to analyze how this process is systematized (hint: it’s not) and what I believe the relationship between data and curriculum, assessment, and instruction is.
Notice – I have cited my sources for some of my claims about the disconnect between district and building leadership…
Using Assessment to Drive Instruction
Mandinach and Jackson identify two different forms of assessment from which to collect data: formative, or assessment for learning; and summative, or assessment of learning (2012). Formative assessments are generally given in the classroom by the teacher, while summative assessments are more formal, like state testing. Interim assessments are a combination of both formative and summative. They sum up the learning that has been achieved and help steer the teaching in the necessary direction to fill gaps (Bambrick-Santoyo, P., 2010).
One example of a formative assessment is the Bethel School District’s grade 1 reading fluency assessment (Braun, D.H., 2011). First grade educators administered what they and Mandinach and Jackson call “benchmark” assessments of reading fluency at different points in the school year (2012). The data is disaggregated to show gaps in fluency between different groups of students. This helped educators target these groups and identify what these students needed in order to improve their fluency by the end of the year (Braun, D.H., 2011).
The Oregon Reading Assessment is a summative assessment that is used in Bethel School District to measure student learning. Data from the 2002-03 school year indicated that only 51% of students were proficient in reading in the Bethel School District. This informed district leaders of the need to shift academic focus to reading instruction and improve practices. In the 2007-08 school year, proficiency jumped to 70% (Braun, D.H., 2011).
District Practices: Standards-Based Teaching and Learning
The Mandinach and Honey conceptual framework for data-driven decision-making maps out how the authors envision the use of data to drive decision making in districts and schools (Mandinach, E.B & Jackson, S.S., 2012). For the Seattle Public Schools district, some of the data Mandinach and Honey identify as integral to this process are missing. Mandinach and Honey’s framework suggests data should be organized and collected, analyzed and summarized, synthesized and prioritized, and then a decision should be made. It also suggests that more data should be collected after the decision is implemented to measure impact and fed into a feedback loop of collecting, analyzing, synthesizing, decision making, implementation, and impact (2012).
Seattle Public Schools provides the first few steps to this process, but there are no data available about implementation, impact, or reassessment of the decision-making process. Seattle Public Schools has had a focus on racial equity and closing “opportunity gaps” for nearly a decade, but it appears they have been looking at the same types of data while expecting different results (Seattle Public Schools, n.d.).
Seattle Public Schools is committed to standards-based teaching and learning to the detriment of their students of color. Disparities in achievement continue to grow along racial and ethnic lines because of the fact the district continues to rely on standardized test scores so heavily as a measure of achievement (Morton, N., 2018). The new strategic plan that says the district will, “unapologetically address the needs of students of color who are furthest from educational justice,” also says they will achieve this, in part, by, “[d]elivering high-quality, standards-aligned instruction across all abilities and a continuum of services for learners;” however, there is no indication the standards that have failed students of color for decades will be changed in any way (Seattle Public Schools, n.d.). The same standards will be used hoping for different results. This could be attributed to the fact there is no feedback loop present in Seattle Public Schools data-driven decision-making framework.
Abbott’s framework of improvement and readiness has the same components as the Mandinach and Honey framework, but it adds a “collaboration” and an “internalize” component to their process of data-driven decision making (Mandinach, E.B. & Jackson, S.S., 2012). This is in line with Michael Fullan’s writing on systematizing decision making and building collective capacity to use data to inform system reform (2010). Seattle Public Schools, however, is lacking the ability to “internalize” or systematize any kind of consistent, effective use of data-driven decision making because it does not have a solid foundation for collaboration between various stakeholders in the system. Mandinach and Jackson identify the following components of a successful “data culture,” in which each component interacts with and acts on each other: leadership (district and school), resources, vision, data culture, professional development, data system and tools, data coaches, data teams, common planning time (2012).
Of the identified components of a successful data culture system, Seattle Public Schools struggles with nearly all of them. Most schools do have data/literacy coaches. Some have data teams, but professional development and common planning time continue to be sporadic, disconnected, and fought over. In the last two bargaining years, the Seattle Education Association has fought with the district to provide common planning and collaboration time for educators across the district and consistent racial equity professional development. Although it is currently in the collective bargaining agreement, educators regularly report their administrators deny access to inter-district collaboration opportunities.
Seattle Public Schools employs a strategy called “site-based decision making” which gives building administrators considerable power to either implement or not implement district strategies. Principals can do their own data analysis and decision making that may or may not align with district goals. For example, the district has officially made K-12 ethnic studies a strategy for closing gaps between white students and students of color (Seattle Public Schools, n.d.). The district collected and analyzed the racially and ethnically disparate data, identified ethnic studies as a solution based on data that indicate improved outcomes for all students (Sleeter, C.E. 2011), resolved to implement ethnic studies, and yet there is no collaboration or systemization of ethnic studies because of the disconnect between district and building goals.
The ethnic studies program manager has reached out to 29 of the 101 principals in the 2018-19 school year to build a relationship and provide support. Only four schools have agreed to pilot the ethnic studies program thanks to the activism of educators in the building, not because of principal leadership. Nine of the principals have not returned calls or emails. Two schools are in planning phase and will not open until the 2019-20 school year. The remaining principals declined to implement ethnic studies for various reasons (Gill, T., 2019).
Mandinach & Jackson have identified leadership, both district and school, as the cornerstone of a data-driven decision-making culture (2012). The disconnect between district and school leadership creates a fractured foundation upon which it is impossible to build collective capacity and collaborative practice in a data-driven process. The disconnect also makes effective systemic implementation of decisions impossible.
Describing the Relationship
It is challenging at this point to adequately describe the relationship between assessment, data, and instruction. Mandinach & Jackson make this claim stating, “data-driven decision making is seen as an emerging field. . . Research cannot measure what has not been implemented broadly or deeply (2012).” If Seattle Public Schools is an indicator of how data-driven decision-making is “implemented,” it cannot be said that data-driven decision making has been implemented “broadly or deeply.”
Bambrick-Santoyo claims data can inform educators about how to transform their instruction to better meet the needs of their students. Data and anecdotes are used to support this claim, but the data is not disaggregated by race or ethnicity (2010). Increases in achievement on standardized tests, which are measure of standards-based teaching and learning, can be great and still leave behind students of color. In fact, schools are winning awards in Washington State for “closing achievement gaps,” while simultaneously leaving behind students of color (Gill, T., 07.12.2018).
Dr. Ibram X. Kendi asserts the current standards-based model of teaching, learning, and assessment is inherently racist (2016). If the standards are racist from the beginning, the outcome of the decision-making based on the analysis of the data will be racist. From this perspective, the relationship between assessment, data, and instruction is one that perpetuates oppression for people of color and maintains the white supremacist status quo. This perspective is supported by history and research other than Dr. Kendi’s. Dr. Wayne Au has written extensively about the relationship between the racist, pseudoscience of eugenics and standards-based teaching, learning, and assessment (2009).
Dr. Bernhardt, in her book, Data, Data Everywhere; Bringing all the data together for continuous school improvement, indicates that relying only on standardized test outcomes to drive teaching and learning is faulty, and district and schools need to also be looking at climate data for students, as well as other, social factors (2016). Gill argues that even when climate and other social factors are used to drive teaching and learning, the people determining the parameters and purpose of the data are not racial equity literate enough to collect data specific to the needs and experiences of students of color (03.11.2018). The fact that 89.9% of educators in Washington State are white (OSPI, 2016) and approximately 80% of educators nation-wide are white (Geiger, A., 2018) support this claim.
In a district that proclaims it will, “unapologetically address the needs of students of color who are furthest from educational justice,” it may be necessary to reassess the relationship between assessment, data, and instruction and standards-based teaching and learning. Doing more of the same is not working, and that is supported by the data.
Au, W. (2009). Unequal by design (critical social thought). New York, NY: Routledge.
Bambrick-Santoyo, P. (2010). Driven by data: A practical guide to improve instruction. San Francisco, CA: Jossey-Bass.
Bernhardt, V.L. (2016). Data, data everywhere; Bringing all the data together for continuous school improvement. New York, NY: Routledge.
Braun, D.H. (2011). Bethel School District results [PowerPoint presentation].
Fullan, M. (2010). All systems go: The change imperative for whole system reform. Thousand Oaks, CA: Corwin, A SAGE Company.
Geiger, A. (27.08.2018). America’s public school teachers are far less racially and ethnically diverse than their students. Pew Research Center. Retrieved fromhttp://www.pewresearch.org/fact-tank/2018/08/27/americas-public-school-teachers-are-far-less-racially-and-ethnically-diverse-than-their-students/
Gill, T. (03.11.2018). When the devil IS the data. Teacher Activist. Retrieved fromhttps://teacheractivist.com/2018/11/03/when-the-devil-is-the-data/
Gill, T. (07.12.2018). Schools of distinction awards ceremony keynote. Teacher Activist.Retrieved from https://teacheractivist.com/2018/12/07/schools-of-distinction-awards-ceremony-keynote/
Gill, T. (01.2019). Matrix. Retrieved from https://docs.google.com/document/d/1_qgaIi5h_ikFEksjT9MdBjkfIhTeQfyh1PhtgpW6Qyg/edit?usp=sharing
Kendi, I.X. (20.10.2016). Why the academic achievement gap is a racist idea. Black
Perspectives. Retrieved from https://www.aaihs.org/why-the-academic-achievement-gap-is-a-racist-idea/
Mandinach, E.B., & Jackson, S.S. (2012). Transforming teaching and learning through data-driven decision making. Thousand Oaks, CA: Corwin.
Morton, N. (12.01.2018). Racial equity in Seattle schools has a long, frustrating history – and it’s getting worse. Seattle Times. Retrieved from https://www.seattletimes.com/education-lab/racial-equity-in-seattle-schools-has-a-long-frustrating-history-and-its-getting-worse/
OSPI. (03.10.2016). Key facts about Washington public Schools. Office of the Superintendent of Public Instruction. Retrieved from http://www.k12.wa.us/AboutUs/KeyFacts.aspx
Seattle Public Schools. (n.d.). Initiatives and core commitments. Seattle Public Schools. Retrieved from https://www.seattleschools.org/district/district_quick_facts/initiatives
Sleeter, C.E. (2011). The academic and social value of ethnic studies; A research review.National Education Association. Retrieved from http://www.nea.org/assets/docs/NBI-2010-3-value-of-ethnic-studies.pdf