1 Notes on methodology 4. 3 Gender Schools Local Authority Region... 12

Transcription

1 The Royal Society Computing Project KS4 NPD data on gender, pupil premium, BAME, EAL and course choices in computing Peter EJ Kemp University of Roehampton April 2017 Contents 1 Notes on methodology 4 2 KS2 results and computer science 5 3 Gender Schools Local Authority Region Pupil premium Local authorities Region BAME Local authorities Region EAL Local authorities Region Computing cohort size - longitudinal study 29 peter.kemp@roehampton.ac.uk 1

2 LIST OF FIGURES LIST OF FIGURES List of Figures GCSE computer science local authorities gender representation - London focus GCSE computer science regional gender representation GCSE computer science local authorities pupil premium representation GCSE computer science local authorities pupil premium representation GCSE computer science regional pupil premium representation GCSE computer science local authorities BAME representation GCSE computer science local authorities BAME representation - London focus GCSE computer science regional BAME representation GCSE computer science local authorities EAL representation GCSE computer science local authorities EAL representation - London focus GCSE computer science regional EAL representation KS4 computing qualification trends - total participants PyeTait schools, KS4 computing qualification trends - total participants KS4 GCSE computer science qualification trend - participant % by Region PyeTait schools, KS4 GCSE computer science qualification trend - participant % by Region KS4 computing qualification trends - Female % PyeTait schools, KS4 computing qualification trends - Female % KS4 computing qualification trends - Pupil Premium % PyeTait schools, KS4 computing qualification trends - Pupil Premium % KS4 computing qualification trends - BAME % PyeTait schools, KS4 computing qualification trends - BAME %

3 LIST OF TABLES LIST OF TABLES List of Tables 1 KS2 Maths profiles of 2016 GCSE cohorts. 45 largest s GCSE computer science provision by school gender characteristic GCSE computer science mixed gender provision GCSE computer science all schools, top 30 providers ( cohort size >= 20) GCSE computer science local authority top female provision GCSE computer science Regional female provision GCSE computer science local authority top pupil premium provision GCSE computer science regional pupil premium provision GCSE computer science local authority top BAME provision GCSE computer science regional BAME provision GCSE computer science local authority top EAL provision GCSE computer science regional EAL provision

4 1 NOTES ON METHODOLOGY 1 Notes on methodology The data analysed here is from the following sources: Student demographic information from the DfE National Pupil Database: all that are linked to one or more results. Student results information from the DfE National Pupil Database: all results with a matching student record, exams taken in School characteristic information from Edubase. Depending on the grouping variable used, tables may show different totals. This is because the grouping variable information is not complete for all and schools, and where data is missing those or schools have been excluded from the summaries. Tables showing % of taking A-level and GCSE are showing the number of taking A-level or GCSE Computing out of all who take any A-level or GCSE. The health of the fields used in this report are as follows: Gender, 0% missing. Pupil premium, 9.8% missing. Ethnicity, 9.2% missing. EAL, 8.4% missing. KS5 demographic data is very incomplete and where possible data has been brought across from matching KS4 student datasets for Whilst possible for student circumstances to change, namely gender and pupil premium, it is assumed that these descriptors will match their KS4 profile. ICT AS entry numbers differ from JCQ data, the JCQ methodology is unclear: they might combine multiple year s data to produce their tables, or combine multiple s into one category. Overall total trends match the JCQ. Computer science data is very close to JCQ numbers. Several tables contain data that would allow for the recognition of 5 of fewer. Following the DfE guidance on this, certain values have been anonymised and rounding has been applied throughout the report to prevent cross tabular student number calculations. All student level data has been rounded to the nearest 5, except in the case where the number would be rounded down to 5, in this case the number has been rounded to 6 to distinguish it from the X figure explained below: X denotes a value that means 5 or fewer could be recognised for a school, local authority or Region. Or a percentage that represents 5 or fewer instances. In the case that several schools have been collected together, e.g. in the case of local authorities, X is given when the number of < number of schools + 5 ; this anonymises all schools in that particular collection. Y denotes a value that is 5 or fewer smaller than the largest value possible, or a percentage that represents 5 or fewer instances. For example in the case that a school has 100 and 97 of them are categorised as BAME, the BAME value would be replaced by Y to anonymise the 3 who are White British. NA denotes a field that has no data stored against it for the school or collection. For example, an area with only one school offering computer science, where the school is an independent school. As independent schools don t return data on pupil premium, this area would be put down as NA. The school tables below have been filtered so that only larger cohort sizes are shown and these numbers have been rounded to the nearest 5. This serves two purposes, firstly it avoids situations where the statistics of very small institutions, which are much more susceptible to the influence of small demographic changes, dominate the top of the tables. Secondly, combining anonymised school level data with anonymised local authority or regional data might allow for recogition of 5 or fewer. As a result, beyond the provided tables, school level data has been redacted from this report and where shown, all numbers have been rounded as described above. Where individual schools have been analysed, there are several schools that have no name or decriptive data stored about them in edubase. These schools can be identified by a URN of value and do not appear on Edubase. Provision in these schools makes up less than 0.1% of all computing provision and they have been included in this report for completeness. We await the DfE s confirmation about their status. 4

5 2 KS2 RESULTS AND COMPUTER SCIENCE 2 KS2 results and computer science Table 1: KS2 Maths profiles of 2016 GCSE cohorts. 45 largest s Subject Mean SD Latin Chemistry Physics Biology Economics German Computing Spanish French Music Psychology Statistics Applications of Maths Bus Studies English Lang History Physical Ed Geography Applied Engineering English Lit Relig Studies D&T Graphics Maths Sociology ICT Drama ALL D&T Prod Des Office Technology Fine Art Dance Science Additional D&T Textiles Social Science: Citizenship Media/Film/Tv D&T Res Mat Art & Design Home Economics: Food D&T Food Tech Art & Design (Textiles) Photography Science Core Health & Social Care Home Economics: Child Development English Lang Lit

6 3.1 Schools 3 GENDER 3 Gender 3.1 Schools Gender Table 2: 2016 GCSE computer science provision by school gender characteristic Schools Students Subject Providers Providers % Subject Students Students % Average Cohort Size Mixed Girls Boys s

7 3.1 Schools 3 GENDER Gender Type Computing Providers Table 3: 2016 GCSE computer science mixed gender provision Female computing Male computing Providers with no females Percentage of providers Mixed Independent Mixed State Non Selective Mixed State Selective s

8 3.1 Schools 3 GENDER Table 4: 2016 GCSE computer science all schools, top 30 providers ( cohort size >= 20) URN Name Gender Characteristic Heathrow Aviation Engineering UTC % of total Mixed Islamia School for Girls Girls Manshead School Mixed Teignmouth Community Mixed School, Exeter Road Queensbury Academy Mixed UTC Swindon Mixed The Magna Carta School Mixed Bedlingtonshire Community Mixed High School UTC Reading Mixed University Technical College Mixed Norfolk Range High School Mixed WMG Academy for Young Engineers Mixed Bristol Technology and Engineering Mixed Academy Dyke House Sports and Technology Mixed College The Studio School Liverpool Mixed Millom School Mixed Whittington Green School Mixed Maltings Academy Mixed Woodcote High School Mixed St Benedict s Catholic High School St Edmund Arrowsmith Catholic Centre for Learning (VA) Mixed Mixed Hugh Baird College Mixed The Grange School Mixed Lincoln UTC Mixed The Vyne Community School Mixed De Aston School Mixed Queen Elizabeth s Grammar Mixed Alford - A Selective Academy Discovery School Mixed The Langley Academy Mixed The Boston Grammar School Boys TOTAL

9 3.2 Local Authority 3 GENDER 3.2 Local Authority Name Table 5: 2016 GCSE computer science local authority top female provision providers Comp providers Comp Sch with females Female % of total Female % of City of London Tower Hamlets Croydon Hartlepool Sutton Kingston upon Thames Islington Lewisham Birmingham Knowsley Bournemouth Trafford Wolverhampton Wirral Salford Hammersmith and Fulham Barking and Dagenham Bexley Bromley Sefton Central Bedfordshire Newham Slough Stoke-on Trent Kingston upon Hull City of Wandsworth Harrow Herefordshire Greenwich Southwark TOTAL

10 3.2 Local Authority 3 GENDER [1] For the following map the City of London (100% of computer science are female) and Isle s of Scilly (0% of computer science are female) have been removed so that differences in provision can be more % clearly seen. 10

11 3.2 Local Authority 3 GENDER % Figure 1: 2016 GCSE computer science local authorities gender representation - London focus 11

12 3.3 Region 3 GENDER 3.3 Region Table 6: 2016 GCSE computer science Regional female provision Name providers Comp providers Comp Sch with females Female % of total Female % of London West Midlands North West North East South East East of England Yorkshire and The Humber East Midlands South West TOTAL

13 3.3 Region 3 GENDER % Figure 2: 2016 GCSE computer science regional gender representation 13

14 4.1 Local authorities 4 PUPIL PREMIUM 4 Pupil premium 4.1 Local authorities Table 7: 2016 GCSE computer science local authority top pupil premium provision Name providers Comp providers Comp Sch with PP PP % of total PP % of Islington Tower Hamlets Camden Westminster Newham Hackney Haringey Knowsley Enfield Manchester Salford Kingston upon Hull City of Southwark Lambeth Hartlepool Halton Waltham Forest Ealing Kensington and Chelsea Oldham Birmingham Wandsworth Greenwich Liverpool Stoke-on Trent Nottingham Southampton Middlesbrough Hammersmith and Fulham Barking and Dagenham TOTAL

15 4.1 Local authorities 4 PUPIL PREMIUM % Figure 3: 2016 GCSE computer science local authorities pupil premium representation 15

16 4.1 Local authorities 4 PUPIL PREMIUM % Figure 4: 2016 GCSE computer science local authorities pupil premium representation 16

17 4.2 Region 4 PUPIL PREMIUM 4.2 Region Name Table 8: 2016 GCSE computer science regional pupil premium provision providers Comp providers Comp Sch with PP PP % of PP for region PP % of PP for London North East North West West Midlands Yorkshire and The Humber East Midlands South West East of England South East TOTAL

18 4.2 Region 4 PUPIL PREMIUM % Figure 5: 2016 GCSE computer science regional pupil premium representation 18

19 5.1 Local authorities 5 BAME STUDENTS 5 BAME 5.1 Local authorities Note: figures for Redcar and Cleveland, Halton, and Isles Of Scilly have been removed due to small, identifiable numbers. Name Table 9: 2016 GCSE computer science local authority top BAME provision providers Comp providers Comp Sch with BAME BAME % of total BAME % of Brent Newham Westminster Ealing Harrow Lambeth Hackney Redbridge Hounslow Enfield Slough Haringey Islington Kensington and Chelsea Waltham Forest Southwark Lewisham Wandsworth Leicester Greenwich Hammersmith and Fulham Barnet Camden Hillingdon Merton Luton Barking and Dagenham Croydon Birmingham Kingston upon Thames TOTAL

20 5.1 Local authorities 5 BAME STUDENTS % Figure 6: 2016 GCSE computer science local authorities BAME representation 20

21 5.1 Local authorities 5 BAME STUDENTS % Figure 7: 2016 GCSE computer science local authorities BAME representation - London focus 21

22 5.2 Region 5 BAME STUDENTS 5.2 Region Table 10: 2016 GCSE computer science regional BAME provision Name providers Comp providers Comp Sch with BAME BAME % of BAME for region BAME % of BAME for London West Midlands South East East of England Yorkshire and The Humber East Midlands North West South West North East TOTAL

23 5.2 Region 5 BAME STUDENTS % Figure 8: 2016 GCSE computer science regional BAME representation 23

24 6.1 Local authorities 6 EAL STUDENTS 6 EAL 6.1 Local authorities Note: have been removed due to small, identifiable numbers. Name Table 11: 2016 GCSE computer science local authority top EAL provision providers Comp providers Comp Sch with EAL EAL % of total EAL Newham Westminster Ealing % of Tower Hamlets Harrow Hounslow Leicester Camden Redbridge Haringey Brent Hammersmith and Fulham Islington Enfield Lambeth Luton Wandsworth Slough Barnet Greenwich Oldham Waltham Forest Hackney Barking and Dagenham Merton Hillingdon Peterborough Kingston upon Thames Kensington and Chelsea Southwark TOTAL

25 6.1 Local authorities 6 EAL STUDENTS % Figure 9: 2016 GCSE computer science local authorities EAL representation 25

26 6.1 Local authorities 6 EAL STUDENTS % Figure 10: 2016 GCSE computer science local authorities EAL representation - London focus 26

27 6.2 Region 6 EAL STUDENTS 6.2 Region Table 12: 2016 GCSE computer science regional EAL provision Name providers Comp providers Comp Sch with EAL EAL % of EAL for region EAL % of EAL for London West Midlands South East Yorkshire and The Humber East Midlands East of England North West South West North East TOTAL

28 6.2 Region 6 EAL STUDENTS % Figure 11: 2016 GCSE computer science regional EAL representation 28

29 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY 7 Computing cohort size - longitudinal study For the following tables and graphs Computing 1 is defined by any under the names: Applications, Applied ICT, Computer Appreciation / Introduction, Computer Architecture / Systems, Computer Games, Computer help, Computing, D&T Sys & Control, desk Operations, Electronic / Electrical Engineering, Handling & Interpreting Data, ICT, Keyboarding, Music Technology (Electronic), Office Technology, Systems / Network Management, WebSite Development value focus All GCSE Computing All non GCSE Computing GCSE Computer Science GCSE ICT Year Figure 12: KS4 computing qualification trends - total participants 1 Note: government guidance documents still list GCSE computer science as computing 29

30 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus All GCSE Computing All non GCSE Computing GCSE Computer Science GCSE ICT Year Figure 13: PyeTait schools, KS4 computing qualification trends - total participants 30

31 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus East Midlands East of England London North East North West South East South West West Midlands Yorkshire and The Humber Year Figure 14: KS4 GCSE computer science qualification trend - participant % by Region 31

32 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value 10 5 focus East Midlands East of England London North East North West South East South West West Midlands Yorkshire and The Humber Year Figure 15: PyeTait schools, KS4 GCSE computer science qualification trend - participant % by Region 32

33 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus All GCSE Computing All non GCSE Computing All s GCSE Computer Science GCSE ICT Year Figure 16: KS4 computing qualification trends - Female % 33

34 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus All GCSE Computing All non GCSE Computing All s GCSE Computer Science GCSE ICT Year Figure 17: PyeTait schools, KS4 computing qualification trends - Female % 34

35 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY 30 value focus All GCSE Computing All non GCSE Computing All s GCSE Computer Science GCSE ICT Year Figure 18: KS4 computing qualification trends - Pupil Premium % 35

36 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus All GCSE Computing All non GCSE Computing All s GCSE Computer Science GCSE ICT Year Figure 19: PyeTait schools, KS4 computing qualification trends - Pupil Premium % 36

37 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus All GCSE Computing All non GCSE Computing All s GCSE Computer Science GCSE ICT Year Figure 20: KS4 computing qualification trends - BAME % 37

38 7 COMPUTING COHORT SIZE - LONGITUDINAL STUDY value focus All GCSE Computing All non GCSE Computing All s GCSE Computer Science GCSE ICT Year Figure 21: PyeTait schools, KS4 computing qualification trends - BAME % 38