Professional Learning Opportunities

The Carnegie Foundation’s Professional Learning Opportunities are structured to help education professionals develop a deep and action-oriented understanding of networked improvement science. Networked improvement science brings together two powerful ways of organizing improvement efforts—improvement science and networked communities—to address the equity challenges in our educational organizations.


Learning Opportunities

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Introduction to Networked Improvement Basics

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.

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Learning Lab

The Learning Lab is a seven-month program designed to guide participating teams to initiate and launch their own networked improvement community (NIC).

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Future Opportunities

Facilitating Improvement Teams

Facilitating Improvement Teams is an 8-10 month professional learning program that builds the capacity of improvement coaches through a blend of in-person and virtual learning experiences. Through instruction from improvement science experts, hands-on opportunities to apply improvement coaching strategies, and engagement in a cohort of like-minded learners, participants will:

  • Understand the foundational knowledge behind improvement science in a network setting
  • Facilitate and coach school-based teams to use improvement science to get results against problems of practice
  • Teach improvement science tools and practices
  • Effectively communicate the ideas of improvement science

Leading Networked Improvement Science

The Leading Networked Improvement Science program is an 8-10 month professional learning program that delves deeply into learning about networks and improvement science in order to prepare participants to lead in networked improvement communities. Through a blend of in-person and virtual learning experiences, participants will:

  • Take on the role of a leader in a network hub
  • Cultivate a strong network community capable of learning together
  • Build structures and routines to support network health and sustainability
  • Flexibly apply the principles and tools of improvement science to structure distributed learning to achieve the network’s aim
  • Leverage network data in multiple ways to guide network learning and provide evidence of improvement

 


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