Creating a Data Culture

This week I was asked to write about my thoughts on data culture. Before I started this class I would have just written that data culture makes me want to vomit, but after learning thus far I realize I only felt that way because SPS has been doing it SO WRONG! I have a deeper appreciation for the power of data – when it’s the right data and it’s created and analyzed by the right people.


Data Culture and Collaborative Inquiry

In creating a data culture, the science of andragogy needs to be considered. Andragogy is the practice of teaching and learning for adults. Knowles, Holton, and Swanson explain how collaboration and need to know are central to the practice of andragogy. They argue that first, adult learners must understand why they are learning a new concept and how it will improve their experiences, then they need to work collaboratively toward solutions while learning the new concept (2015).

When creating a self-sustaining professional learning community (PLC), collaboration is key, as it creates a sense of group ownership in the work, learning, and accountability. DuFour and Fullan posit that data PLCs must be the driving force behind systemic change (2013). For this to happen, all stakeholders must be active in the work. Dr. Bernhardt states that if a school or district is to have an effective data culture, all data must be analyzed, not just data that indicate gaps (2016). Again, this approach requires collaboration on levels that does not frequently exist in schools.

How to Create a Sustainable Data Culture

            Some of the literature on successful data culture indicates that data literacy is the key to effective, sustainable data culture. There are two types of data literacy according to Mandinach and Jackson: being able to read and use data to change practice and being able to assess the validity of the data. They argue data are only as good as the tools and instruments used to collect them (2012). While this makes sense, Bernhardt argues that to best engage educators in data is to challenge them to discover data that is missing (2016).

Bernhardt’s argument is aligned to the inquiry-based approach to andragogy. Simply giving data to educators and telling them what they need is the antithesis of the science behind andragogy. Knowles, Holton, and Swanson identify six characteristics at the core of adult learning (2015):

  1. Learners need to know why, what, and how.
  2. Self-concept of the learner is autonomous and self-directing
  3. Prior experience of the learner is utilized.
  4. The readiness to learn is dependent on how the learning is related to the life of the learner.
  5. The learning must be problem-centered and contextual.
  6. The motivation to learn must be intrinsic and must be linked to personal gain.

If the process of creating a data culture starts with tasking educators with finding data they are missing, that starts the process with self-directed autonomy. It provides space for educators to determine the why of learning which is dependent on their prior experience, readiness to learn, and their professional need and context. When educators are looking for data that will address very specific needs to improve their instruction, it is more likely their motivation will be intrinsic.

Starting with what is missing makes it easier to have a PLC in which educators are leading instead of building or district leaders. Dr. Bernhardt believes that putting principals or other people in authority as leads in PLCs prevents true, collaborative, inquiry-based planning (2016). Professional Learning Communities that start with authority figures are what DuFour and Reeves call “PLC Lite.” PLCs lite are characterized by “[m]eetings that only address standards, that focus entirely on disciplinary issues and parent complaints, or that center on [human resource] issues” (2016). Since school principals and other authority figures are evaluated on these topics, it makes sense they center data about them. These issues, however, rarely relate to daily teacher and student experiences nor do they inform how to change instruction to meet the needs of students.

Instead, DuFour and Reeves suggest the following characteristic for student and instruction-centered PLC work (2016):

  1. Collaborative teams that take collective responsibility for student learning
  2. Establish a viable, student-centered curriculum
  3. An assessment process that includes frequent, team-developed, common formative assessments based on point 2 above
  4. Use the data collected from formative assessments to identify students who need help
  5. Create a system of interventions that do not remove students from primary instruction

DuFour and Reeves’ emphasis on formative assessments is supported by Bernhardt’s emphasis on the same (2016) and is in line with andragogical theory about context and linking learning to personal gain (Knowles, et al, 2015). Summative assessments, like high stakes testing data, does nothing to help educators adjust instruction during learning. Data from formative assessments are the most appropriate for use in creating a sustainable data culture.

Examples of Effective and Ineffective Data Culture in Seattle Public Schools

Most data analysis work in Seattle Public Schools is centered on high stakes testing and discipline data, and most data analysis work starts with gaps in existing data. The language in the mission statement of the district explicitly names gaps: “Seattle Public Schools is committed to eliminating opportunity gaps to ensure access and provide excellence in education for every student” (Seattle Public Schools, n.d.c). The mission statement mandates that educators start with gaps in the data culture. This is in direct conflict with prevailing wisdom on how to create a data culture (Bernhardt, 2016).

The mission statement also infers that students lack access to opportunities instead of educators lack the ability to meet needs of their students. Data from Seattle Public Schools indicate that even when students of color have access to opportunities (higher socioeconomic status), they still achieve at the same levels of white students who have less access to opportunities (Seattle Public Schools, n.d.a). By naming a gap in students’ opportunities, we remove the onus from educators to do deep, reflective work on their practice.

More student-centered language (Fullan, 2011) would be, “Seattle Public schools is committed to providing excellent education for every student by providing anti-racist, culturally responsive educators for every student.” The latter statement puts direct accountability on the district and educators for doing personal, reflective work to meet the needs of students, regardless of the students’ “opportunities.” It still addresses the racial disparities by calling out anti-racism and culturally responsive practices while shifting the deficit from students’ “opportunities” to educators’ practice.

An additional factor to consider is that the research and evaluation team in Seattle Public Schools consists primarily of white people, with the director being a white male (Seattle Public Schools, n.d.b). When Bernhardt writes about finding data that is missing, this is one crucial piece of datum. When the goals of the district, including the Strategic Plan, specifically call out racial disparities, it would make sense that the data culture be led by a person who identifies with impacted groups. A racial equity literate (Gorski, 2015) person of color would be best suited to set the parameters and purpose of data collected than a white male who cannot fully understand the needs of students of color.

An example of a successful data culture exists in Seattle Education Association’s Center for Racial Equity. This is a department committed to furthering racial justice in the school district operated by the educators’ union. The Center is directed and operated by mostly women of color. Most members are currently classroom teachers. The director is a teacher on special assignment (Seattle Education Association, 2019b). While this group uses district data as a starting point, they frequently do a gap analysis of the existing data, looking for what is missing. Since most of the members are people of color, they have a better sense of the needs of students of color and the types of data to look for.

Within the Center is another group called Racial Equity Team Coaches. This is a group of mostly women of color who are currently classroom educators that work to support the data analysis of racial equity teams across the district (Seattle Education Association, 2019a). The success of this program can be attributed to the fact that the coaches are educators of color who are doing the work they expect others to do. Educators are leading the work instead of administrators which leads to increased experiences of efficacy (Dunn, Airola, Lo, & Garrison, 2013), if only vicariously through the coaches at first. Those being coached, however have been observed to respond better to their peer coaches than to administrator coaches.

References

Bernhardt, V.L. (2016). Data, data everywhere; Bringing all the data together for continuous

school improvement (2nd ed.). New York, NY: Routledge.

DuFour, R. & Fullan, M. (2013). Cultures built to last: Systematic PLCs at work. Bloomington,

IN: Solution Tree Press.

DuFour, R. & Reeves, D. (2016) The futility of PLC Lite. Phi Delta Kappan, 97(6), 69-71. DOI:

10.1177/003172171663687.

Dunn, K. E., Airola, D. T., Lo, W-J., & Garrison, M. (2013). Becoming data driven: The

influence of teachers’ sense of efficacy on concerns related to data-driven decision making. The Journal of Experimental Education, 81(2), 222–241. DOI: 10.1080/00220973.2012.699899.

Fullan, M. (2011). Change leader; Learning to do what matters most. San Francisco, CA: Jossy-

Bass, A Wiley Imprint.

Gorski, P. (03.2015). Equity literacy for all. Education Leadership. Retrieved from

http://www.edchange.org/publications/Equity-Literacy-for-All.pdf

Knowles, M.S., Holton III, E.F., & Swanson, R.A. (2015). The adult learner: The definitive

classic in adult education and human resource development (8th ed.). New York, NY: Routledge.

Mandinach, E.B. & Jackson, S.S. (2012). Transforming teaching and learning through data-

driven decision making. Thousand Oaks, CA: Corwin.

Seattle Education Association. (2019a). RET Partner Program. Seattle Education

Association. Retrieved from https://www.seattlewea.org/center-for-race-equity/ret-partner-program/

Seattle Education Association. (2019b). SEA’s Center for Racial Equity. Seattle Education

Association. Retrieved from https://www.seattlewea.org/center-for-race-equity/

Seattle Public Schools. (n.d.a). Eliminating opportunity gaps. Seattle Public Schools. Retrieved

from https://www.seattleschools.org/cms/One.aspx?portalId=627&pageId=14245065

Seattle Public Schools. (n.d.b). Research and evaluation. Seattle Public Schools. Retrieved from

https://www.seattleschools.org/cms/one.aspx?pageId=15164

Seattle Public Schools. (n.d.c). Strategic Plan. Seattle Public Schools. Retrieved from

https://www.seattleschools.org/district/district_quick_facts/strategic_plan

Published by

Tracy Castro-Gill

Seattle Public Schools Ethnic Studies Program Manager 2019 PSESD Regional Teacher of the Year Seattle University Anti-Racist Pedagogy Instructor PhD Student

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