No abstract

No abstract

The rapid turnover of novice teachers is a stubborn challenge plaguing schools across the country. The field has come to some consensus about key elements of effective novice teacher support that have potential to ameliorate this problem, although this knowledge has been applied in an inconsistent fashion. Beginning teacher support is a complex issue that functions on many levels: It impacts teachers, school administrators, districts, and the educational system and labor market more broadly. This article analyzes a collaborative effort to tackle this problem: the Building a Teaching Effectiveness Network (BTEN). We use a qualitative case study approach to analyze how BTEN schools supported new teacher development using a standard feedback process and improvement science methods. This analysis offers evidence that these methods enabled participants to learn about their schools while enacting and enhancing the teacher support process, and to reckon with persistent norms that can be obstacles to creating improvement in schools.

No abstract

This article focuses on “measurement for improvement,” which is the analytic work critical to making and spreading effective changes in quality improvement approaches to system transformation. Quality improvement methods aim to trigger and accelerate the learning of people within a system to make that system work better. This is accomplished through the careful attention of those who experience and enact the system at various levels and by leveraging the expertise from within and outside of the system. Measurement provides critical fuel for that learning. “Measurement for improvement” serves as an umbrella term encompassing a range of measures discussed by quality improvement scholars. These other terms include “process measures,” “practical measures,” and “pragmatic measures.” Data about system outcomes are often a key motivating factor for spurring improvement efforts, for example, low students’ graduation rates or low achievement test scores. However, outcome measures typically fall short of the data needed to inform the day-to-day work of trying changes in practice and learning from these change efforts. Instead, measures of key processes that lead to system outcomes are needed. Measures for improvement need to be closely connected to key processes, timely, and easy to collect and analyze on a regular basis by people in the system. They must also function within social processes that engender trust and transparency so that improvers can learn from failures as well as successes. The education sector has looked to quality improvement efforts in health care as a model for this work, and this article draws heavily on key texts from quality improvement in health care. However, there are some key differences in the types of data regularly available between the fields of health care and education; these differences prompt attention to certain measurement concerns, which are taken up in the references included in this article. The article begins with History and Lineage, which includes some key references that trace quality improvement ideas from industry to health care to education. The next section, Features of Measures for Improvement covers how the function of measures in a quality improvement endeavor shapes the form they take. The next section, A Set of Measures to Inform Improvement, discusses the types of measures needed in combination to inform quality improvement work in systems. The following section, Rigor of Measures to Inform Improvement, addresses what it means to have rigorous measures for improvement, in the context of the educational field where the phenomena of interest are often difficult to see or count. Another challenge of measurement for improvement is taken up in the next section, Analytic Infrastructure for Measurement for Improvement. The challenges of data collection, analysis, and consumption within the busy work lives of improvers imposes constraints and considerations to the social and technological infrastructure that enables the use of measures for improvement purposes. Finally, this article concludes with cases that illustrate measurement for improvement in educational contexts.