Document Type

Dissertation

Degree

Doctor of Education

Major

Education

Date of Defense

5-28-2025

Graduate Advisor

Dr. Katherine O'Daniels

Committee

Dr. Nancy Singer

Dr. Shea Kerkhoff

Dr. Julie Sheerman

Abstract

Abstract

Currently, the day-to-day data collection and analysis work of teachers is often only deemed valuable if it is easily observed and quantitatively recorded. Teachers are asked to produce results with an incomplete set of tools and are then personally blamed — instead of blame being placed on the structural system — when the desired results don’t manifest. This participatory action research (PAR) study challenged the status quo by asking: What if teachers were empowered to expand their definition of data to include the rich, qualitative data at their fingertips, and what if teachers’ experience and intuition were valued instead of dismissed in making sense of data and determining action steps? Within the PAR structure, the participants (five high school English teachers) acted as co-researchers alongside me to investigate our qualitative data practices over the course of a year. In analyzing the data using thematic coding, we found that our definition of data grew over the course of the study to include more qualitative sources; we also found that we had established tools, processes, and systems for using qualitative data sources for data-driven decision-making (DDDM). By purposefully complicating the popular deficit narrative and highlighting often-ignored or devalued evidence, a different story about student and teacher performance emerged, one that speaks to a need for radical dreaming and symmetry between teacher and student experiences in school. As we engaged in the process, the benefits of PAR to develop our adaptive expertise and data literacy became evident. The implications include professional learning (PL) considerations for educators, such as supporting the expansion of teachers’ definition of data to include qualitative data sources, honoring teachers’ intuition and experience in data sensemaking processes, and using a PAR structure in designing PL.

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