Micro-pathways are two or more stackable credentials (21st century skills included) validated by employers that lead unemployed, displaced, and low-wage workers to median-wage occupations and on a path to a degree.
Cohort 1 colleges have focused on adult learners as their primary target audience. Data shows these are the majority of learners that enroll in noncredit courses. They are more likely to be older: The average age of students in noncredit programs is 34 compared to 22 for students in credit programs; more likely to have a GED rather than a high school diploma; and more likely to be students of color*. With that in mind, Cohort 1 intentionally designed their micro-pathways to begin with noncredit programs. This provides adult learners an entry point into postsecondary education and a bridge to higher credentials and degree programs on the credit side. However, this has meant bridging the noncredit-credit divide typical at community colleges.
As stated by Dr. Ian Roark, Vice Chancellor of Workforce Development & Innovation at Pima Community College: “Equity is really at the center of all of this work. Everything we do in higher ed that hierarch-alizes the learner, and even otherizes them, especially when you put “non”-in front of a learner and call them a ‘noncredit’ learner, we have other-ized them. That’s why we have embraced this vision of the new majority learners that EDL has taught us to embrace and bring about in the context of equity.”
Pima and the other Cohort 1 colleges have embraced micro-pathways as a gateway to community college transformation.
Below are five of their accomplishments in aligning noncredit and credit.
1. Noncredit micro-pathways courses + credentials articulate to credit programs.
For CCGEF, Cohort 1 colleges put the onus on themselves to align competencies and assessments to ensure credentials and courses completed in noncredit programs are credit-worthy, rather than learners having to prove themselves through additional assessments or other Prior Learning Assessment (PLA) activities. This was accomplished through articulation of mirror or mirrored courses (which are the same courses offered in credit and noncredit), industry certification crosswalks and equivalency agreements.
2. Learners can enter and exit micro-pathways at their own pace.
Cohort 1 noncredit micro-pathways provide an on-ramp to a credit career pathway and the opportunity to earn higher credentials. Learners can move along the career pathway at their own pace, and enter and exit at different points along the pathway as their career goals dictate. For example, many learners can move into employment after completing the micro-pathway, but can choose to return to earn a higher- level credit certificate and/or degree as their personal and professional career goals dictate. These pathways and entry and exit options were communicated to learners in advising, on institution websites, and through infographics.
3. Colleges are developing a culture of ‘a learner is a learner,’ regardless of where the journey begins.
Cohort 1 design teams have worked to overcome the typical division in support services offered to noncredit learners. Two of the colleges have established formal advising programs for learners who start on the noncredit side and others are doing this on a more informal basis through faculty members who oversee both noncredit and credit pathways. One college has set up a co-enrollment process with their local workforce system to ensure learners have access to tuition assistance and wrap-around services — services that would normally only have been offered on the credit side. Colleges are also providing noncredit learners access to work-based learning opportunities and scholarships, with new funds established specifically for CCGEF learners.
4. CCGEF colleges launched a Data Collaborative to better understand learners.
Cohort 1 launched the Data Collaborative with partners Brighthive, the National Student Clearinghouse, Urban Institute, and Credential Engine. Cohort 1 wants to learn more about their noncredit learners, including whether they matriculate into credit-bearing programs or disconnect from the college after completing noncredit courses. The Data Collaborative’s goals are to yield valuable information about learners, credential completion, employment and wage data, among other items.
5. Colleges are scaling their noncredit and credit alignment through micro-pathways design.
For each of the Cohort 1 design teams, micro-pathways have served as a way to innovate around noncredit and credit alignment. Most of the teams have been learning and iterating on a handful of programs but have plans to scale across the college. For example, Prince George’s Community College designed and launched three micro-pathways and added a fourth early in 2022. Pima Community College launched eight micro-pathways and added another, with plans to scale even further during 2022.
The progress Cohort 1 has made is tremendous, yet if you ask any of the design teams, they will say there is still more work to be done. They would like to see more resources to support noncredit advising models and a greater focus on marketing to noncredit learners. The Lab is grateful to have partnered with our six colleges and systems and their dedication to serving new majority learners.
To learn more about Cohort 1 and the Community College Growth Engine Fund, download: Design Insights Brief: Community College Growth Engine Fund Micro-pathways: A Gateway to Community College Transformation.
This article by Valerie Taylor is part of the Lab’s work helping community colleges innovate and transform through the micro-pathways design process. Learn more about the Community College Growth Engine Fund here, subscribe to our email newsletter for updates, and follow along on Twitter: #Micropathways.* Citation: Xu, D., & Ran, X. (2015). Noncredit education in community college: Student, course enrollments, and academic outcomes. Community College Research Center, 2015. Available: https://ccrc.tc.columbia.edu/media/k2/attachments/noncredit-education-in-community-college.pdf