Developing Vertical Data Teaming and Data-Driven Instructinoal Decision Making in a Rural School Setting

Rural schools often experience limited instructional resources, professional isolation, and inconsistent structures for collaborative, data-driven decision-making. This dissertation in practice addressed a local problem of practice by strengthening teachers’ instructional decision-making through the development of vertical data teaming in a rural elementary school. Guided by two research questions, the study examined how vertical teaming influences teachers’ data use and instructional decisions and how structured data analysis supports planning for targeted interventions and enrichment. The study employed a design-based school improvement model with embedded elements of insider action research. Grounded in Mintrop’s (2016) improvement framework, a six-week improvement cycle was implemented through weekly, data-driven collaborative sessions across grade levels. The intervention included research-based tools, ongoing data collection, and practitioner-led professional development focused on vertical alignment and content-specific instructional practices in mathematics and reading. Findings indicate that structured vertical data teaming improved teachers’ confidence, collective efficacy, and effectiveness in using student data to inform instruction. The study concludes that practitioner-led, data-driven collaboration supports instructional improvement in rural school contexts.