Student demographics
1 Introduction
This section of the report looks at the demographic details of the students taking GCSE and A level qualifications computer science between 2013 and 2020. In addition to the well known under representation of females in GCSE and A level computer science , this work aims to look at provision of the subject by free school meal provision, special educational needs provision and ethnicity. In doing so it provides a more nuanced look at demographics taking computer science qualifications at these levels and builds on previous work by Kemp, Berry, and Wong (2018).
2 Methodology
To create the analysis below, data from the Department for Education’s National Pupil Database (NPD) was used. Using the yearly key stage 4 and key stage 5 results tables, that collect data on all exams sat each year, the GCSE and A level results were extracted. GCSE students are typically aged 15-16 and A level students are typically aged 17-18. These results were matched against all students in the corresponding yearly census to provide demographic details. Any results without corresponding demographic data have been removed from the analysis. Data on gender (Male and Female), whether a student had received free school meals in the last 6 years as an indicator of student poverty (also known as pupil premium), special educational needs (SEN) provision, and ethnicity is explored below. SEN provision has three categories:
| code | name | GCSE | Alevel | ||
|---|---|---|---|---|---|
| students | per | students | per | ||
| 1_NON | No identified SEN | 477180 | 86.2% | 152720 | 95.0% |
| 2_SNS | SEN without a statement/EHC plan | 62840 | 11.3% | 7320 | 4.6% |
| 3_SS | SEN with a Statement/EHC plan | 13860 | 2.5% | 780 | 0.5% |
| data from 2020, rounded to nearest 20 students | |||||
Ethnicity is broken into two levels, major ethnic grouping and minor ethnic grouping. These levels can be described as below:
| code | name | GCSE | Alevel | ||
|---|---|---|---|---|---|
| students | per | students | per | ||
| AOEG | Any other ethnic group | 10320 | 1.9% | 3480 | 2.2% |
| ASIA | Asian | 60220 | 10.9% | 23460 | 14.6% |
| BLAC | Black | 32560 | 5.9% | 9980 | 6.2% |
| CHIN | Chinese | 1940 | 0.4% | 1200 | 0.7% |
| MIXD | Mixed | 29320 | 5.3% | 8720 | 5.4% |
| UNCL | Undeclared | 7600 | 1.4% | 2880 | 1.8% |
| WHIT | White | 411900 | 74.4% | 111100 | 69.1% |
| data from 2020, rounded to nearest 20 students | |||||
| code | name | GCSE | Alevel | ||
|---|---|---|---|---|---|
| students | per | students | per | ||
| ABAN | Asian_Bangladeshi | 10240 | 1.8% | 3700 | 2.3% |
| AIND | Asian_Indian | 16000 | 2.9% | 7860 | 4.9% |
| AOTH | Asian_Other | 10100 | 1.8% | 4760 | 3.0% |
| APKN | Asian_Pakistani | 23900 | 4.3% | 7140 | 4.4% |
| BAFR | Black_African | 21000 | 3.8% | 7380 | 4.6% |
| BCRB | Black_Caribbean | 7360 | 1.3% | 1480 | 0.9% |
| BOTH | Black_Other | 4200 | 0.8% | 1140 | 0.7% |
| CHNE | Chinese_Chinese | 1940 | 0.4% | 1200 | 0.7% |
| MOTH | Mixed_Other | 11020 | 2.0% | 3660 | 2.3% |
| MWAS | Mixed_WhiteAsian | 6620 | 1.2% | 2480 | 1.5% |
| MWBA | Mixed_WhiteBlackAfrican | 3680 | 0.7% | 980 | 0.6% |
| MWBC | Mixed_WhiteBlackCaribbean | 8000 | 1.4% | 1600 | 1.0% |
| NOBT | NotObtained | 4000 | 0.7% | 1680 | 1.0% |
| OOTH | Other | 10320 | 1.9% | 3480 | 2.2% |
| REFU | Refused | 3620 | 0.7% | 1200 | 0.7% |
| WBRI | White_British | 378820 | 68.4% | 101020 | 62.8% |
| WIRI | White_Irish | 1660 | 0.3% | 760 | 0.5% |
| WIRT | White_IrishTraveller | 160 | 0.0% | 20 | 0.0% |
| WOTH | White_Other | 29980 | 5.4% | 9260 | 5.8% |
| WROM | White_Romany | 1280 | 0.2% | 40 | 0.0% |
| Alevel data from 2020, rounded to nearest 20 students | |||||
Note that the DfE categorises Chinese students separately from Asian students, with Asian mainly defining students who are ethnically Bangladeshi, Indian or Pakistani.
The analysis consists of two types of graph that show trends across years:
- Overall percentage of student groups taking GCSE or A level in computer science
- Percentage of student groups within schools offering the GCSE or A level computer science, plotted alongside a dashed line showing the overall population of that student group within schools offering the the subject.
To avoid the recognition of individuals, all counts have been rounded to the nearest 20. Missing data, coded NA has been removed from the analysis, as have ethnicity codings for NOBT, REFU and UNCL.
3 Limitations
- the computer science GCSE was first examined in 2013, having been introduced into schools before the curriculum change. This makes it likely that the cohort of this early provision was skewed towards schools with teachers with a particular interest in computer science.
- other computing related exams exist, such as the level 2 qualification in iMedia and the level 3 BTEC qualification in computing, but have not been analysed here.
- data in this report currently only goes up to 2020, further data that will bring this report up to date is currently being analysed
- student results data for 2020 had all school identifier information redacted by the DfE due to the pandemic, this means graphs that show the provision in schools offering the GCSE in 2020 are showing data for the whole population, rather than those schools that offered the subject.
- ethnic groupings are limited to those provided by the DfE, these groupings match those used by the UK census.
- free school meal provision is a rough indicator of parental wealth, other potentially better indicators could give different results
- SEN provision is a rough indicator of student need and functioning, other potentially better indicators could give different results
- the gender of students used below is recorded by the DfE as only male and female.
- free school meal data is missing from the NPD for A level students.
4 Gender
4.1 GCSE
Whilst the overall representation of females in the GCSE computer science cohort has increased since the subject’s introduction, numbers remain low. Whilst more boys still attend schools that offer the GCSE, the representation is more balanced than in the early years of the qualification, where boys made up ~54% of the cohort. This might have been due to all boys schools being over represented in the schools that were first to offer the GCSE.
Overall A level provision has been increasing since the curriculum change. In 2013 roughly 0.1% of females taking one A level or more chose to take the A level in computer science, this rose to 1.2% in 2020. In line with overall trends in A level provision, more females are now in providers that offer computer science than males.
4.2 A level
5 Poverty
Students from poorer backgrounds have been consistently less likely to take the GCSE than their richer peers. In 2019 14.6% of those not receiving free school meals took the subject, compared to 11.3% of those that did receive free school meals.
5.1 GCSE
6 Special Educational Needs (SEN)
Students with any SEN provision are less likely to take the GCSE in computer science than those without, as well as being less likely to be in providers that offer the subject.
For A level, students with SEN provision were consistently more likely to take computer science than those without. However, this data is only for students who are taking one or more A level. The overall number of students with special educational needs statements / EHC plans (3_SS) taking A levels was 780 in 2020, down from 12,320 in the GCSE cohort of 2018. This suggests that roughly 6% of students with statements who took GCSEs went on to take A levels. There were no instances of missing SEN provision data in the A level 2020 data set, with 11% of GCSE students having this field absent. However, assuming most SEN provision would likely be correctly recorded as funding is attached to it, the missing data seems likely to be 1_NON. Due to the rounding to the nearest 20 present in this data, the 2020 computer science 3_SS figure of 80 / 780 (10.3%), falls within the range of 71 / 789 (9%) to 89 / 771 (11.5%); this is still above the figure for those without special educational needs provision, which was 4.5%. It remains unclear what type of special educational need these students have.
6.1 GCSE
6.2 A level
7 Ethnicity
Chinese students are more likely to take GCSE computer science than any other ethnic group, with 28% of them taking the qualification in 2020, compared to 19% of Asian students and just 12% of Black students. The number of white students has been consistently beneath what you would expect if they were representative of White student population within schools offering the GCSE - in 2020 the population of these schools was 74% White, with 69% of the computer science cohort being White.
There were large differences in provision within these major ethnic groupings, for example 24.4% of Asian:Indians took the qualification in 2020, compared to 15.2% of Asian:Pakistani; 12.7% of Black:African compared to 8.7% of Black:Caribbean; and 11.8% of White:British compared to 15.5% of White:Other and 3.1% of White:Roma.
Chinese students were also the mostly likely group to take the A level in computer science, with 8.3% of this grouping sitting the exam in 2020, compared to just 4% of Black students. White student representation was much closer to their population in the providers offering the subject. Within the major ethnic groupings 5.6% of Asian:Indians took the qualification in 2020, compared to 3.6% of Asian:Pakistani; Black:African and Black:Caribbean both had 4.1% of their population taking the subject, and 4.4% of White:British took the A level compared to 6% of White:Other; White:Roma numbers were so low that they could not be reported, with only 40 students overall in all A level providers.
7.1 GCSE
7.2 A level
8 Intersectional: gender & ethnicity
Female chinese students were less likely to be taking the GCSE than their male peers, 16% vs 38.3%, however, they were still the largest female group. The main difference between the trends in the overall ethnicity figures and the combined gender ethnicity figures is the under representation of white females, of which only 4.5% took the GCSE in 2020. White females made up 60.2% of the female computing cohort in 2020, much lower than the population in schools offering the subjects, which had white females making up 74.2% of the female cohort
For the A level Figure 8.4, the number of females from non-white backgrounds were so low in 2013 as to need to be redacted from this study. Rounding to the nearest 20 and suppressing students numbers below 10 results in the redaction of all other female ethnicities other than white students for 2013, and white and Asian students for 2014, making the small figure data for these earlier years unreliable.
8.1 GCSE
8.2 A level
9 Intersectional: gender & poverty
Female and male uptake of the GCSE has been consistently lower for free school meal students than their wealthier peers.
Free school meal data is not present in the A level dataset.
9.1 GCSE
10 Conclusions
- Female uptake remains a concern at GCSE and A level.
- the early years of the GCSE saw more boys than girls in schools that offered the GCSE.
- free school meal male and female students are less likely to take the GCSE than their wealthier peers.
- GCSE students with SEN provision are less likely to take the GCSE in computer science. At A level this group is more likely to take the qualification, however, the majority of SEN students do not progress to A level qualifications, making the overall population of this group small.
- Chinese female and male students are the best represented ethnicity groups in the GCSE and A level.
- within ethnic groupings, there are large disparities in uptake of the GCSE.
- White girls and Black boys are the least well represented ethnic groups at GCSE.
11 Further work
- the functioning and diagnosis profiles of those SEN students choosing to take the GCSE
- the cultural reasons for differences in uptake of computer science amongst different ethnicities and ethnic gender groups.
- include poverty proxies when studying uptake of the A level.
12 Acknowledgements
- The English Department of Education’s National Pupil Database team for providing the data.
- This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research data sets which may not exactly reproduce National Statistics aggregates.
- The analysis was carried out in the Secure Research Service, part of the Office for National Statistics.
- All responsibility for inferences and conclusions in this report lie with its authors.