Traditions of Quality Improvement in Education

by

Paul G. LeMahieu, Cierra S. Cooper

• LAST MODIFIED: 26 February 2020
• DOI: 10.1093/obo/9780199756810-0242

Introduction

The past twenty years have seen increasing interest in the tools, processes, and methodologies of improvement research in education. Variously termed continuous improvement, quality improvement, and the like, these approaches to systematically improving practice comprise (in their best instantiations) a science of educational improvement. Collectively, these approaches put good ideas into practice effectively (producing positive impact as improvements against persistent problems of practice), reliably (providing improvements from all practitioners and for all students), and at scale (realizing improvement across widespread and diverse contexts). To the framers of these approaches to improvement, it is important that they realize the stature of a scientific enterprise. This is because of the rigor and consistency that a scientific approach can impart but also because it produces knowledge about the improvement of practice that will have impact and benefit at scale and provides knowledge that is held in high regard beyond the boundaries of just those who produced it. This requires adherence to the essential elements of a bona fide scientific endeavor: shared definitions and understandings of foundational concepts and the relationships amongst them; epistemic practices for inquiry that are public, transparent, and open to widespread inspection and understanding; and communal curation of the scientific knowledge thusly produced. For the purpose of this article, we apply the definition of improvement science offered in Learning to Improve: How America’s Schools Can Get Better at Getting Better by Bryk, et al. The defining characteristics include: (1) make the work problem focused and user centered—focus on understanding the problem to be solved and its root causes: (2) variation in performance is the problem to solve—identify and work to eliminate unwanted variation in processes and outcomes; (3) see the system to improve it—see how local conditions shape work processes to define (and tolerate) the problem, understand the system within which improvements must succeed; (4) you cannot improve at scale what you cannot measure—collect common measures of outcomes, processes, and secondary consequences to see if changes are improvements, track progress toward aim; (5) accelerate improvement by embracing rigorous inquiry—engage in rapid cycles of inquiry to learn fast and improve quickly; and 6) accelerate learning by tapping the power of networks—use the wisdom of the collective to learn, accomplish, and spread improvements. In this article, the authors have identified six major traditions of IS that have garnered considerable interest and application in education. These include: networked improvement; design-based implementation research; Implementation Science; Lean for education; Six Sigma; and Positive Deviance. For each of these traditions, we offer a brief description of the approach followed by annotated bibliographic references that include germinal writings and at least one case example of its use in education.

Networked Improvement

The Networked Improvement Model integrates two sets of ideas and their related practices: continuous quality improvement with its roots in the processes, tools, and methodologies of Deming and Shewhart and carefully structured and supported networks as a unique organizational form in which to conduct continuous improvement. The resulting Networked Improvement Communities (NICs) engage in the conduct of continuous quality improvement by applying improvement science methodologies within three broad and overlapping phases of activity: (1) Chartering (in which a specific problem of practice is studied, along with the system that gives rise to that problem, and a theory of practice improvement and design principles for potential solutions are promulgated); (2) Knowledge production (in which members of the NIC identify, prototype, and test potential solutions to refine them and ultimately warrant them as improvements); and (3) Spread (in which the improvement knowledge produced throughout the NIC is gathered, organized, and made available for widespread uptake and use. Bryk, et al. 2015 articulates the fundamental principles of a Networked Improvement Science. In doing so, they draw upon early germinal work by Deming 1986 and Shewhart 1986 who had an early role in defining the field and developing many of its core tools and processes. Deming and Shewhart worked in business and industry—primarily manufacturing. Their work was later adapted by Langley, et al. 2009 and IHI 2003 to the health-care field, a people-serving field and professional environment more closely akin to and therefore encouraging of its application in education. Engelbart 2003 and Nielsen 2011 provide the conceptual bases for scientific networks engaging improvement work: the makeup, organization, and structural supports that enable them to be productive improvement networks. Cohen-Vogel, et al. 2016 as well as Hannan, et al. 2015 provide examples of empirical work within this tradition of quality improvement. LeMahieu, et al. 2017 chronicle the history of these ideas prior to entry into and then within the education field. They provide an historical perspective as well as characterization of the approach in education comparing it to other common approaches to continuous quality improvement. They also draw upon van Campen, et al. 2013 to illustrate the approach through an example of its application in education.

• Bryk, Anthony S., Louis M. Gomez, Alicia Grunow, and Paul G. LeMahieu. 2015. Learning to improve: How America’s schools can get better at getting better. Cambridge, MA: Harvard Education Press.

  Using ideas adapted from long-standing traditions in the improvement sciences, Learning to Improve shows how a process of disciplined inquiry can be combined with the use of networks to identify, adapt, and successfully scale up promising interventions in education. Rather than “implementing fast and learning slow,” the authors illustrate how educators can adopt a more rigorous approach to improvement that allows the field to “learn fast to implement well” (p. 17).

• Cohen-Vogel, Lora, Marisa Cannata, Stacey A. Rutledge, and Allison Rose Socol. A model of continuous improvement in high schools: A process for research, innovation design, implementation, and scale. Teachers College Record 118.13 (2016).

  This article describes a model for continuous improvement comprising three phases: (1) prototyping a program to embody essential core elements that have demonstrated efficacy; (2) rapid iterative testing of the prototype to adapt it to local context and conditions; and (3) explicit partnering between researchers and practitioners to facilitate the spread of improvement knowledge.

• Deming, W. Edwards. 1986. Out of the crisis. Cambridge, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study, Cambridge, MA.

  Deming offers a theory of management based on his famous Fourteen Points for Management. Management’s failure to plan for the future brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend but also by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service.

• Engelbart, Douglas C. 2003. Improving our ability to improve: A call for investment in a new future. Lecture presented at the IBM Co-Evolution Symposium, San Jose, CA, September 24–25.

  Argues that our criteria for investment in innovation are, in fact, short-sighted and focused on the wrong things. Engelbart proposes, instead, investment in an improvement infrastructure that can result in sustained, radical innovation capable of fundamentally changing processes and systems and expanding the kinds of problems that we can address through those processes and systems.

• Hannan, M., J. L. Russell, S. Takahashi, and S. Park. 2015. Using improvement science to better support beginning teachers: The case of the Building a Teaching Effectiveness Network. Journal of Teacher Education 66.5: 494–508.

  Analyzes a collaborative effort to tackle the problem of induction of beginning teachers. It analyzes how participating schools supported new teacher development using a co-constructed feedback process and improvement science methods. The analysis offers evidence that these methods enabled new teachers to enhance their efficacy and learn about their schools while enhancing teacher support processes developed through networked improvement methodologies.

• IHI (Institute for Healthcare Improvement). 2003. The Breakthrough Series: IHI’s Collaborative Model for Achieving Breakthrough Improvement. IHI Innovation Series white paper. Boston, MA: Institute for Healthcare Improvement.

  The findings and tools in these reports provide an opportunity to understand and evaluate persistent problems of practice and begin testing changes that can help organizations make breakthrough improvements. The Breakthrough Series is designed to help organizations close the gap between desired and actual performance by creating a structure in which engaged organizations can easily learn from each other and from recognized experts in problem areas where they want to make improvements.

• Langley, Gerald J., Ronald D. Moen, Kevin M. Nolan, Thomas W. Nolan, Clifford L. Norman, and Lloyd P. Provost. 2009. The improvement guide: A practical approach to enhancing organizational performance. 2d ed. San Francisco, CA: Jossey-Bass.

  While exploring Langley and colleagues’ Model for Improvement, this guide offers an integrated approach to process improvement that delivers rapid and substantial results in quality and productivity in diverse settings.

• LeMahieu, P. G, A. Grunow, L. E. Nordstrum, and L. Baker. 2017. Networked improvement communities: The discipline of improvement science meets the power of Networks. Quality Assurance in Education 25.1: 5–25.

  This paper presents the historical development, theoretical foundations, core principles, and practical adaptation of key elements of the NIC model for quality improvement in education. The case study of the paper specifically examines the problem of fostering new teacher effectiveness and retention in large US public-school systems.

• Nielsen, Michael. 2011. Reinventing discovery: The new era of networked science. Princeton, NJ: Princeton Univ. Press.

  Argues that we are at the threshold of a dramatic change in science driven by new social tools, cognitive processes, and technological tools that support productive collective action in extensive and widely dispersed social networks. It documents through example and illustrates through analyses how the internet is enhancing our collective intelligence, expanding our problem-solving capabilities, and increasing our capacities to engage in collaborative scientific exploration.

• Shewhart, Walter. 1986. Statistical method from the viewpoint of quality control. Chicago: Dover Publications.

  In this classic volume, Shewhart defines quality control and its historical background. He explicates statistical techniques supporting a science of improvement: the study of variability and establishment of tolerance limits related thereto; presenting measurement in original and summarized forms; the importance of predictive validity in improvement contexts; and the problems of accuracy and precision, operational meaning, as well as the quantity and nature of evidence needed to warrant judgments.

• van Campen, J., Sowers, N., and Strother, S. 2013. Community College Pathways 2012–2013 descriptive report. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.

  This report documents the initiation, development, and implementation of the Carnegie mathematics pathways. It explicates the use of Networked Improvement Science as the means of designing, refining, and spreading the program. It also provides detailed analyses of data describing implementation and its impacts after two years.