“Grass ceiling” masks the educational barriers faced by rural children

A new study has shed light on the “grass ceiling” of hidden school underachievement in rural areas of England. The research, conducted by Luke Graham from the University of Exeter, challenges the traditional narrative that urban pupils perform less well compared to their rural counterparts.

While exam performance data shows that rural pupils outperform those in cities, the study argues that educational outcomes are not necessarily better in rural areas. The official data fails to account for geographical sparsity in the countryside, and the statistics are skewed by the fact that rural pupils generally come from more advantaged homes.

When data is considered in a more nuanced way, it becomes evident that rural pupils actually perform less well in many measures of educational outcomes. Moreover, there are extreme gaps between their achievement and those from more privileged homes in the same area.

The study calls for policymakers to use more sophisticated measures of rurality and disadvantage to understand the reasons behind the differing performance of rural pupils and to determine what actions should be taken to address the issue.

Mr Graham explains that the small number of rural pupils in the total school population becomes “hidden” in the GCSE national data, which is highly influenced by the relatively high numbers of more affluent rural pupils. This has led to the persistent patterns of rural underperformance being overlooked.

The study emphasizes the need for changes to make the gap in attainment needs more visible, as masking rural underachievement has resulted in children in the countryside missing out on interventions. This has influenced educational policy and impacted national funding and resourcing decisions.

To support schools in coastal and other remote communities, the study suggests using measures of remoteness or inaccessibility. Additionally, providing access to sparsity data within the Department for Education’s national database would quickly enable a more nuanced data analysis both nationally and within rural areas.


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