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Introduction to Networked Improvement Basics℠ is an 11-week (12-session), mediated online course that will familiarize participants with key concepts, strategies, and tools in improvement science. Learners will work in cohorts to develop their conceptual understanding of the principles and practices of improvement science and build their skills to apply improvement methods to real problems.
This course introduces an approach for attending to the work of an improvement network and is intended for network leaders and individuals who support the analytic activities of networks, such as data analysts, researchers, or evaluators. In the course, participants will use the Evidence for Improvement framework to consider a network’s activity related to its working theory of improvement, collaboration among its partners, concerns about scaling, and interaction with its environmental context. Course activities emphasize the importance of simultaneously engaging with the work in each of these areas and the implications that doing so has for the collaboration between a network leader and an analytic partner(s).
Improvement Reviews are a formal, protocol-driven process in which senior improvement specialists review the activities and progress of a NIC or improvement project, with particular attention given to the use of improvement methodologies. Reviews of networks can also attend to the health and effectiveness of the network as a social learning organization engaged in improvement science. Improvement reviews provide a “critical friendship” that can maximize improvement learning and the achievement of aims by a project team or NIC. Learn more about Evidence for Improvement: An Integrated Analytic Approach for Supporting Networks in Education
This visual depicts three levels of nested activity of an improvement network. The levels offer a way to understand the different activities of a network.
This report offers an integrated perspective on how a variety of tools and practices, drawn from diverse forms of program evaluation, can help to inform the leaders of improvement networks in advancing productive change. It describes the evidence that helps to make activities at each of these levels visible, the role of an analytic partner in supporting activity at each level, and the dispositions, skills and knowledge that enable this work.
"Networked Improvement Communities are scientific professional communities distinguished by four characteristics. They are: 1. guided by deep, shared understanding of the problem, the system that produces it, and a shared theory of improvement. 2. focused on a well-specified common aim. 3. disciplined by the rigor of improvement science. 4. coordinated to accelerate developing and learning about changes and their effective integration into practice across varied educational contexts. The Network Development Framework describes the technical core of activity that grounds the network together as well as a social structure that supports the community working and learning together.
The paper, The Social Structure of Networked Improvement Communities: Cultivating the Emergence of a Scientific-P
This preconference session, offered as a workshop each spring in advance of the Carnegie Summit on Improvement in Education, provides an introduction to the dispositions, core concepts, and tools of improvement science in education.
Using ideas borrowed from improvement science, the authors show 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. Organized around six core principles, the book shows how “networked improvement communities” can bring together researchers and practitioners to accelerate learning in key areas of education. Examples include efforts to address the high rates of failure among students in community college remedial math courses and strategies for improving feedback to novice teachers.
One example of an improvement in knowledge product or service is the use of artificial intelligence (AI) in educational technology. AI-powered educational platforms, such as adaptive learning systems, can analyze a student's performance and provide personalized feedback and recommendations for improvement. These systems can also track student progress and provide teachers with real-time data on student performance, allowing them to quickly identify areas where students need extra support.