Why do parents opt to send their children to academically selective or fee-paying schools? Of course there are many reasons, however one of the main reasons is increased achievement. This is what we wanted to focus on in our study. We wanted to find out whether school type adds anything to the prediction of exam scores at 16, over and above those factors schools use to select their intake.
What do I mean by selection factors? I mean factors that schools can use to select their intake. In the UK, we have three main school types: state schools that are non-selective, state schools that select pupils using an entrance test (called ‘grammar’ schools), and private schools, fee-paying schools that often select their intake using an entrance test.
In our study, we wanted to find out whether the type of school a student attends makes a difference to their exam grades, or whether the selection process itself is the reason selective school students get better grades. Do selective schools merely select more able students? In addition, because the factors schools use in selecting students are shown to be heritable, we also wanted to explore whether there were any genetic differences between students attending different school types.
To answer these questions, we used the UK-based Twins Early Development Study (TEDS). TEDS is an incredibly valuable study that has followed twins born in England and Wales in 1994-1996 to the present day. As well as collecting data on behavioural, psychiatric and education-related traits, researchers also collected DNA on over 4,000 individuals in the sample. This allowed us to create ‘genome-wide polygenic scores’ (GPS) for each individual in our sample.
A GPS is an individual-specific score created by summing up the effects of tens of thousands of single nucleotide polymorphisms (SNPs) which have been linked to a trait of interest. SNPs are places in someone’s DNA where people are known to vary by a single base pair. For example, someone may inherit two copies of the nucleotide base guanine (one from their mother and one from their father), or two copies of the nucleotide base adenine, or one of each. To identify whether certain SNPs are linked to differences in traits, we need to use the results from large ‘genome-wide association studies’ or ‘GWAS’. In our study, we used the results from a GWAS of 300,000 people that sought to identify SNPs associated with educational attainment (Okbay, 2016). Armed with the results from this GWAS, telling us which SNPs were associated with educational attainment, and how large the association was, we created a GPS for each person in our sample. Individually the SNPs have a very small effect, however when you sum all of their effects together, it is more predictive. This process is a bit like summing up items on a test maths; individually each item doesn’t tell you much about how good someone is at maths, but together they give you an indication about their ability. Just like any other variable, we can use the GPS to look at differences between people, or groups of people, as in school types.
What did we find?
We found modest average GPS differences between students attending different school types. Those attending selective schools (grammar and private schools), had higher GPS for educational attainment than those attending non-selective school. This means that students attending selective schools have more genetic variants associated with educational attainment.
GPS plotted means (and 95% confidence intervals) between state non-selective, grammar and private school students. Note: There were significant EduYears GPS mean differences between state non-selective school students and both grammar (t = 4.869, p < 0.001; d = 0.413) and private school students (t = 7.170, p < 0.001; d = 0.372). There was not a significant difference between grammar and private school students (t = 0.436, p = 0.659)
As well as average genetic differences between selective and non-selective schools, we also found average exam score differences between students of different school types. Those attending selective schools achieved a grade higher in their exams at age 16 compared to those attending non-selective schools.
However, when we consider that schools select their intake based on certain factors, such as ability, achievement and, to an extent, socio-economic status, these differences are suddenly less surprising. Ability, achievement and SES have all been shown to be substantially genetically influenced, as well as shown to correlate with later academic achievement. So what would happen if we control for the differences attributable to these factors?
The answer is that differences between school types in terms of average genetic differences disappear. Furthermore, once we account for the selection factors and the GPS to look at achievement differences between school types, these greatly reduce. Indeed, one of our main findings was that after accounting for the variance explained by the selection factors and the GPS, the variance in GCSE explained by school type dropped from 7.1% to only 0.5%, much less than the variance explained by the GPS alone (7%).
So what does this all mean?
The key conclusion from our study is that mean differences in exam performance between school types are largely due to the same factors the schools use to select their intake in the first place. A self-fulfilling prophecy. However, we are aware that there are many reasons why parents opt to send their children to selective schools and we would like to explore these factors in future research.
Finding genetic differences between students of different school types does not have any necessary implications for educational policy, however it shows that when you select for one thing – ability for example – you also passively select for all the factors which correlate with ability, even as far as someone’s DNA. We also think that, with the use of polygenic scores becoming more widespread, and as they get more predictive, it is really important that we start a discussion around the use of genetics in the classroom.