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SHEEO Report Highlights How Data Gaps Mask Rural Inequity

According to a new report from the State Higher Education Executive Officers Association, the biggest challenge of serving rural learners is bad data. The report, titled “Strong Foundations 2025: Insights into State Postsecondary Rural Data,” found no standard definition of who rural learners are, which makes it difficult to reliably collect and effectively use data needed to inform targeted policies.  

Sixty-three percent of state agencies don’t even collect data on rural learners or Rural Serving Institutions, the authors found. Only 18 percent of agencies have a formal, uniform definition for rural learners or institutions. Definitions often vary between using population density or a socioeconomic lens (e.g., "distressed counties"). 

Postsecondary success for rural learners is shaped heavily by place, with those living in education deserts – often defined as 25 miles or further from a broad access institution – facing persistent gaps in bachelor's degree attainment compared to their urban and suburban counterparts. Only about one-quarter of rural residents over age 25 hold a bachelor’s degree, compared to 37% of those in cities and suburbs. 

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The bigger picture: 

A lack of centralized, disaggregated data is often a major roadblock to serving vulnerable populations and addressing persistent inequities – this is true beyond just the conversation about rural students. In January, the Pell Institute discussed the quandary of how not having centralized, consistent tracking of how students use financial aid over time, states lose the ability to see which systemic inequities are persisting, making it nearly impossible to "adjust policies in real time" to support struggling students. And the Institute for Higher Education Policy emphasizes the importance of disaggregated data to identify and close equity gaps in both access and completion. 

A 2021 policy brief by the Education Commission of the States found that persistent equity gaps hide in disaggregated data. For example, lumping all Asian students together often shows high student outcomes, but masks the fact that Hmong and Laotian students may face completion gaps similar to or greater than other marginalized groups. Similarly, focusing only on socioeconomic or first-generation status hides the fact that even when controlling for these factors, attainment gaps persist along racial lines. 

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