Innovation, Implementation Science, and Data-Based Decision Making: Components of Successful Reform

Ever since the 1957 Soviet Union’s launch of Sputnik, it seems the United States has been in a constant state of school reform. That event galvanized the United States to enact reforms in science and engineering education (Powell, 2007), to be followed over the years by a dizzying array of “innovations” in instructional practices (teacher-led, child-centered, Response to Intervention, evidence-based), in structural innovations (small schools, small class sizes, classrooms without walls, charter schools), in personnel preparation (extra years of training, alternative routes to credentials), and in accountability (pay for performance, value-added modeling, changing evaluation procedures). Yet the student achievement data have remained remarkably flat since the 1970s (National Center for Education Statistics, 2011). During this time, educators have seen reform initiatives quickly come and go; researchers have estimated that the average life span of an educational innovation is only 18–48 months (Aladjem & Borman, 2006; Latham 1988). Each of these reform efforts represents an attempt to solve an educational problem. Despite strong evidence of effectiveness when evaluated in research settings, many of these so-called innovations often returned disappointing results when taken to scale. The problem may be not in the innovations themselves but rather in the manner in which they have been implemented (Fixsen, Naoom, Blase, Friedman, & Wallace, 2005).