Journey From Data Into Instruction: How Teacher Teams Engage in Data-Driven Inquiry

Abstract

Increasingly U.S policy-makers and K - 12 schools are adopting data-driven improvement interventions believing that such initiatives support both student and teacher learning. While widespread, studies examining the impact of such data-driven inquiry programs are decidedly mixed, raising questions about what teachers actually do when discussing data. To address this, I followed three teaching teams engaged in data inquiry in order to understand the individual and shared cognitive processes through which teachers make sense of student data to identify gaps in learning and consequently determine instructional responses. In the first paper, I explore how teachers make sense of student data to identify student-learning challenges. I find one team engaged in strategic data use characterized by a precise focus, methodical data collection, and evidenced-based analysis. This process was supported by the team’s deep knowledge of learning targets, high expectations for students and evidence-based discourse about data. In contrast, the second team was unable to devise a rigorous data collection plan and the third team lacked a clear focus for their inquiry. In the second paper, I unpack how teachers plan instruction in response to gaps in student learning noticed in data. For all three teams, I find that collaboration around instructional responses to data operated like a market place: teachers brainstormed ideas together but made individual decisions about what to transport into their classrooms. Furthermore, teachers’ choices were constrained by their shared instructional repertoire. As a result, data inquiry appeared to facilitate teachers targeting instruction to match student needs but did not appear to promote teachers learning new instructional approaches.