Lonely schools: the relationship between geographic isolation and academic attainment

Abstract

Background

School improvement initiatives in England have focused on urban areas, which have traditionally been home to larger numbers of poor and underperforming pupils. Previous research has found that rural regions of the country have had higher overall educational attainment due to their greater affluence. However, that broad picture could be hiding under-serviced and under-performing pupils from disadvantaged backgrounds.

Purpose

The study’s aim was to identify whether all pupils and all disadvantaged pupils attending geographically isolated secondary schools have different academic attainment rates compared with their peers at less isolated schools.

Methods

The isolation of a school is calculated based on the average travel time by car to its five nearest state-funded mainstream secondary schools. This was then included as an independent variable, along with variables for school demographics, prior pupil attainment and neighbourhood deprivation.

Results

Disadvantaged pupils attending more isolated schools had lower attainment rates (as measured by the percentage of students achieving grades of C or higher in English, mathematics and at least three other subjects at General Certificate of Secondary Education [GCSE] level) than pupils in less isolated schools, when controlling for school demographics and prior attainment. There was no relationship found between whole-school GCSE attainment and geographic isolation.

Conclusion

When framing the challenges of providing equitable opportunities in education, broader contexts beyond pupil characteristics, such as geographic isolation, should be taken into consideration.

Keywords:

• Educational isolation
• geography
• travel-time analysis
• regional variations

Acknowledgements

Thank you to Katy Theobald at The Future Leaders Trust for her comments on an earlier draft of this paper and to Meenakshi Parameshwaran at the Education Datalab for her advice and support on statistical methods.

Notes

1. The General Certificate of Secondary Education (GCSE) is a secondary school qualification primarily used in England, Wales and Northern Ireland. The International Standard Classification of Education 2011 classifies GCSEs as an upper secondary qualification (UNESCO Institute for Statistics 2015) that is ‘[s]ufficient for partial level completion, without direct access to post-secondary non-tertiary education or tertiary education’ (UNESCO Institute for Statistics 2012, 41). The proportion of pupils achieving a grade of C or higher in English, mathematics and at least three other subjects at GCSE level is the standard headline secondary school performance measured used by the Department for Education in the years covered by this study (Department for Education 2014, 2015a, 2016c).

2. A dependent variable is defined as the key variable being observed in a statistical analysis, the value of which may change depending on the values of the different independent variables.

3. Disadvantaged pupils are pupils that have been eligible for free school meals at any point in the previous six years, or that have been in the care of the local authority at any point (Department for Education 2016d).

4. Independent variables are characteristics – in this case school demographics and the Index of Isolation – that are hypothesised to have an influence on the dependent variable, in this case the percentage of pupils in a school achieving a grade of C or higher in English, mathematics and at least three other subjects at GCSE level. Including multiple independent variables in this statistical analysis means that the effect of those independent variables on the dependent variable can be controlled for.

5. The OpenStreetMap project is an open source geographic database, using information from contributors and publicly available maps, including Ordnance Survey maps. It is available from http://www.openstreetmap.org

6. R is a computer programming language designed for statistical computing that was used to conduct all the statistical analysis in this paper. The ‘osrm’ package is a collection of functions, akin to a plugin, that allows for queries to be made to the Open Source Route Mapping engine using the R language.

7. Key Stage 4 is the England National Curriculum term for secondary school Years 10 and 11, when pupils are aged 14–16. GCSE exams are typically taken at the end of year 11, coinciding with the end of Key Stage 4.

8. Key Stage 2 is the England National Curriculum term for primary school for Years 3–6, when pupils are aged seven to 11. The Key Stage 2 point score is the average number of points attained by a cohort of students in the Key Stage 2 standardised tests taken at the end of Year 6, the final year of primary school.

9. The exact proportion may have changed in the decade since this paper was published, although at the time of writing I have been unable to locate any research with more up-to-date numbers for England.

10. A variable with only two possible values, in this case ‘True’ if the school is in London and ‘False’ if it is not.

11. Ordinary least squares (OLS) regressions are a frequently used method of statistical analysis that produces an estimate for the relationship between a dependent variable and one or more independent and control variables.

12. A treatment variable in this case is the key independent variable, the Index of Isolation.

13. Standardising adjusts the value of each observation so the mean of each variable is 0, and the standard deviation of each variable is 1. Standardising the variables means the relative effect size of each of the independent variables, which use different scales and units, can be more easily compared to one another.