Improvement Frameworks

Understanding improvement models and other frameworks and how they can be used to support district-wide improvement will support regional providers to make effective use of OIP and OLAC resources. For example, the OLAC module Learning Supports provides information on educational frameworks, as well as methods that support the use of any of the frameworks, such as co-plan to co-serve, differentiating instruction, teaching learning strategies, and more.

Improvement frameworks provide procedures and tools designed to create an infrastructure that improves the capacity of organizations to implement an innovation well. They do not usually dictate the use of particular strategies – other than to encourage the use of evidence-based strategies or practices. Some frequently used models are Improvement Science, Implementation Science, and Collective Impact.

Improvement Science

Improvement science combines insights from improvement initiatives in business and the health sciences
(Bryk, Gomez, Grunow, & LeMahieu, 2015). These insights help practitioners in these fields, and in education as well, develop useful practices that have desired effects in particular settings. Curiously, not all practices that are effective in general are actually effective everywhere. There’s inherent variability. Improvement science works with the variability to find ways to extend the applicability of professional practices (e.g., in business, health science, education, and other fields as well). According to Bryk and associates (2015, p. 475),

[Improvement science] joins together [scientific] discipline… with the power of structured networked communities to accelerate learning to improve. It uses disciplined, analytic, and systematic methods to develop and test changes that achieve reliable improvements. It is inclusive in drawing together the expertise of practitioners, researchers, designers,
technologists, and many others. And it is very deliberate in organizing its improvement activities in ways akin to a scientific community.

Six key principles support improvement based on the Improvement Science approach (Bryk et al., 2015):

  1. Make the work problem-specific and user centered.
  2. Focus on variation in performance.
  3. See the system that produces the current outcomes.
  4. Measure what we are trying to improve at scale.
  5. Use disciplined inquiry to drive improvement.
  6. Accelerate learning through networked communities.

Plan-Do-Study-Act (P-D-S-A) Cycles

Improvement Science operationalizes disciplined inquiry in terms of P-D-S-A cycles. These cycles include four stages of inquiry: (1) a “plan” stage to ask questions and make predictions as well as to plan data collection, (b) a “do” phase in which the plans are executed and during which observations and unexpected problems are documented, (3) a “study” phase in which data analysis takes place and in which the data are compared to the predictions to generate information about the effects of the practice under review, and (4) an “act” stage during which changes to the original plan can be implemented to resolve the unexpected problems. The cycle goes through multiple iterations, always building knowledge.

Networked Improvement Communities (NICs)

Improvement Science uses a particular kind of community of practice to support P-D-S-A cycles as well as sharing of information about the effectiveness of innovations. According to Bryk and associates (2015, p. 145) a NIC provides the “architecture for broad participation” in improvement work, facilitating engagement by breaking down large work processes into smaller subtasks thus “eas[ing] entry for individual participation.” Each NIC focuses on a specific work “target” with the goal of fostering social learning about how to implement an innovation effectively in a local school or district. In many cases, a NIC bridges the divide between research and practice by connecting academics with practitioners. In this way, the NIC accelerates learning across a diverse group of practitioners and organizations. The sharing of experience across the NIC speeds both the accumulation of knowledge and the transformation of that knowledge into practice.

The term peer-to-peer network is sometimes used in place of NIC and describes groups of individuals that engage in collaborative inquiry around common problems of practice. For additional information see, click here.

The following video provides an overview of Improvement Science Framework: 

Implementation Science

This model focuses on a collaborative process for “identifying and solving problems of practice…. [The process involves] reconfiguring the problem space, jointly developing prototypes of analytic tools through iteration and learning and zooming in on the needs of users of research evidence while zooming out to promote systems thinking among key stakeholders” (Metz, 2015p. 1). In addition to other important insights relating to operationalizing practices and implementing them with fidelity, Implementation Science describes stages of implementation and implementation drivers.

Stages of Implementation

According to NIRN, there are four interrelated stages of implementation: (1) exploration, (2) installation, (3) initial implementation, and (4) full implementation:

  • Exploration. At the exploration stage, teams identify needs and options, and make decisions about whether or not to move forward with a particular practice (e.g., instructional strategy, action, intervention). Among the issues the team considers are whether the needs of students are identified, the “fit” of potential practice in relationship to the need, evidence demonstrating the use of the practice, availability of resources to support the practice, the readiness for use of the practice, and the capacity of the team to use the practice as designed and to sustain its use over time.
  • Installation. The installation stage involves creating the infrastructure and putting necessary organizational supports into place for implementing a particular practice. Such activities may include acquiring or repurposing resources needed to implement the selected practice, such as identifying sources for training and coaching, providing initial training for staff, finding or developing assessment tools, and providing access to needed materials, data systems, and equipment.
  • Initial Implementation. At the initial implementation stage, team members are beginning to use agreed-on strategies or actions. NIRN described initial implementation as “the most fragile stage,” where the perseverance required for using new practices sometimes results in team members giving up and returning to the status quo. It’s critical at this stage for the full and effective use of new practices to be supported and accomplishments celebrated. NIRN advises teams to “get started, then get better.”
  • Full implementation. When the “new” practice becomes the standard practice or, more simply, the way business is routinely done, teams have achieved full implementation status. According to NIRN, full implementation is reached when 50% or more of the team use the practice as intended (i.e., with fidelity) and desired outcomes-something that is difficult to achieve without the necessary support.

Implementation Drivers

Stages of implementation are not linear. Rather, teams move between stages as the work evolves and as team members learn new ways of working together. Implementation drivers-defined by NIRN as the key components of the capacity and the functional infrastructure supports that enable a program’s success-affect teams’ ability to implement fully agreed-on practices.

NIRN identified three categories of implementation drivers: (1) competency, (2) organization, and (3) leadership. A brief description of each follows.

  • Competency Drivers. Competency drivers develop, improve, or sustain team members’ and teams’ capacity to implement a particular practice (strategy, action, intervention, etc.) as intended. Coaching, training, and the recruitment and selection of the right personnel for the right positions are examples of competency drivers.
  • Organization Drivers. Organization drivers-such as facilitative administration, data systems that support decision making, and systems interventions-are mechanisms that create and sustain hospitable organizational and system environments for effective implementation.
  • Leadership Drivers. Leadership drivers (e.g., technical and adaptive leadership) focus on providing the right leadership strategies for the types of leadership challenges that often emerge as part of the change process and involve making decisions, providing guidance, and supporting organizational functioning.

The following video provides an overview of the Implementation Science Framework:


Collective Impact

The Collective Impact framework, described by Collaboration for Impact as having a “centralised infrastructure – known as a backbone organisation – with dedicated staff whose role is to help participating organisations shift from acting alone to acting in concert,” is useful in considering Ohio’s SSoS (directed by ODE) as the backbone organization, and the use of OIP as the backbone infrastructure for supporting district-wide continuous improvement.

The following video provides an overview of the Collective Impact Framework:

Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America’s schools can get better at getting better. Cambridge, MA: Harvard Education Press.
Metz, A. (2015). The potential of co-creation in implementation science. Chapel Hill, NC: National Implementation Research Network (NIRN).