Adjustment in Practice

Data-driven instruction, as a theory of action, makes several assumptions: 1) data from standardized assessments is the most valuable form of data and is sufficient to guide teaching, 2) teachers are willing recipients of the mandates surrounding such data, and 3) teachers will effectively apply the pedagogical strategy of backward planning. This chapter presents a collective case study of data-driven instruction by reporting on a two-year investigation of what data-driven instruction means to teachers and administrators, and how these concepts are constructed across the cultures of nine poverty-impacted schools. The authors use the term adjustment in practice, in contrast to fidelity in interpretation, to describe the adaptation necessary when complex innovations are put into place. They argue that adjustment in practice is desirable when well managed, and identify data-driven instruction as a tool that teachers and support staff must negotiate use of by correcting for underlying flaws in the theory of action. The authors provide examples of adaptive and maladaptive adjustment in practice across the school communities observed. The chapter closes by identifying four promising practices in data use: 1) establishing a school-wide community of practice to manage the innovation, 2) ensuring that all data users in the school have a sound understanding of the strengths and limitations of standardized assessments and resulting data, 3) developing internal teams and routines to manage and facilitate data use, and 4) promoting student data use.