Meet online with colleagues and NCWIT staff throughout the academic year in small groups (one person per institution) to learn about strategic planning and how to enact data-informed promising practices. 

Through asynchronous and synchronous sessions, you will learn from and network with peers, consider what data to collect, and be shown a model for how to think holistically about your program, from recruitment to retention. In addition, you’ll be provided with proprietary tools to help bring colleagues on board and to track your department’s progress year after year. Concurrently, participants will work with their committed local institutional teams to create tailored strategic plans to broaden participation in computing (BPC) and ensure success for all students!

Learning Circle Cohorts include participants from a breadth of institutional types, and past participants have come from a variety of college and university contexts.

If you’re interested in participating in Learning Circles, click here.

At no cost, Learning Circle participants receive: 

  • Training and support on how to systematically assess your undergraduate computing program across six dimensions – from recruitment to retention; 
  • Guidance to develop and implement a  research-based strategic plan;  
  • Data visualizations to identify trends and patterns in program entry (applications, acceptances, and matriculations) and student outcomes (enrollment and degree attainment). 

Download an Overview of the Learning Circles: Learn more about the Learning Circles by downloading a handout to share with staff, faculty, and administrators at your institution.

A quote from a Learning Circle Participant reading: Our involvement with the learning circle was essential in bringing our department together to consider what activities would work best for us, then to develop a usable plan for increasing retention and recruitment.

A graphic outlining some of the benefits of Higher Ed Learning Circles.

Learn more about NCWIT Learning Circles

This material is based upon work supported by the National Science Foundation under Grant No. 2216561. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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