Degrees of Freedom: Teachers' Use of Student Performance Data in Decision Making and Instructional Change

Teachers’ data-driven decision making (DDDM) can serve a technical function in guiding instruction. It also is a constitutive practice of the school as a social organization. And in many settings, it has taken on the character of an institutional myth that lends legitimacy in an era of intense pressure for evidence-based practice. The proposed research is intended to develop knowledge about how, if at all, DDM leads to real instructional change. In particular, we will explore the logics of action that guide DDDM in schools and how those logics are enacted through structures, processes, speech, and action. We posit that at least four logics may be in play the logic of feedback and diagnosis, the logic of instructional repertoires, the logic of accountability, and the logic of student voice. For this naturalistic case study, we will select four public elementary schools in New York City. We will study teachers on two grade levels, including at least one grade level where standardized testing is used. We will observe the structures, interactions, processes, and tools and resources that comprise the practice of DDDM and analyze how these components reflect different theories of action for DDDM. We will document the substantive content of DDDM discussions and the instructional decisions and plans that are made. We then will make repeated direct observations of classroom teaching, documenting the instructional changes that follow from DDDM. We will triangulate our observations with interviews with teachers and school leaders. A longitudinal design will enable us to observe how teachers’ participation in DDDM and their efforts to make change in their classrooms evolve over time. Overall, we aim to elaborate an evidence-derived theory of action for how the socially situated practices of teachers’ use of performance data and other information about students have impact in classrooms and schools. These findings from specific cases will, we hope, establish evidence proofs that can shed light on DDDM in other settings.,