Note

Local authority community learning statistics have been removed from this release due to data quality issues. Please see the Quality and methodology information for details on how the data quality issues are being investigated and will be handled.

The data quality issues don’t apply to any community learning data submitted by colleges. Community learning where colleges are the lead provider is still included in this report as part of part-time further education.

Main points

• There were 155,580 learners in further education, apprenticeships or other work-based learning during the 2023/24 academic year.
• Part-time learning numbers are recovering, after a long decline.
• The number of apprenticeships started fell by 5%, compared to the previous year.
• Level 3 apprenticeships are rising, foundation apprenticeships are falling, compared to the previous year.
• More learners are studying at least partly in Welsh.
• There has been an increase in Preparation for Life and Work activities.
• There has been an increase in the percentage of work-based learning taken by learners with ethnic minority backgrounds other than White.
• A levels are less likely to be taken by learners who had experience of deprivation during secondary school.

Learners by learning and provider type

• Description: There were 130,745 learners in colleges. There were 47,855 apprentices, split between colleges and other training providers.
• [Note 1]: Some learners will have studied at multiple provider types.
• Data in Table 1.1 of the accompanying spreadsheet

Further education includes learners studying A levels and other general qualifications, as well as learners studying vocational qualifications (for example BTECs).

‘Other work-based learning’ is made up of transition qualifications for people who work with children in play or childcare settings.

Introduction

1. This report presents an estimate of initial participation in higher education (HE) for the 17 to 30 year old Welsh population for the academic years from 2016/17 until 2022/23.

2. The Higher Education Initial Participation (HEIP) measure is an estimate of the probability that a Welsh domiciled person will participate in HE by the age of 30. The analysis also looks at the difference in the HEIP between males and females. A full explanation of the methodology and data sources is in the methodology section.

3. The statistics in this publication are Official Statistics in Development as we are developing this measure and recognise that there are limitations to the methodology used. By publishing this information as Official Statistics in Development users can be involved in the development of these statistics and to contribute in making them as useful and relevant as possible.

4. We would welcome any feedback on the content of this publication whether relating to the methodology or what information could be included to make this useful for you. To provide feedback please email us at [email protected].

Why we are publishing these statistics

5. One of the strategic duties of Medr as set out in the Tertiary Education and Research (Wales) Act 2022 is to “encourage individuals who are ordinarily resident in Wales, particularly those who have additional learning needs, to participate in tertiary education.” The intention in publishing this measure is to provide some evidence about participation in HE, feeding into the overall information for participation in the wider tertiary education sector.

6. Another strategic duty of Medr is to “promote increased participation in Welsh tertiary education by persons who are members of under-represented groups”. As well as an overall HEIP measure for Wales, this publication also includes a split by sex to compare the initial participation in HE of males and females. While this is the only personal characteristic included here, part of the development of this measure will be to investigate if other characteristics could be included to provide a greater insight into the differences in participation in HE from different groups of the Welsh population.

7. Participation in tertiary education has been an area of increasing focus within the wider Welsh policy environment. In 2022, the Welsh Government commissioned the Welsh Centre for Public Policy to undertake a review evidence and best practice on inequity in tertiary education in Wales. The subsequent report was published in October 2024. In November 2024, the Senedd’s Children, Young People and Education Committee commenced an inquiry into routes into post-16 education and training with a particular focus on participation. The inquiry is ongoing.

8. A measure of initial participation in HE for Wales has not been published since 2016 when the Higher Education Funding Council for Wales (HEFCW) published statistics for the 2012/13 academic year. During this time participation measures for England, Scotland and Northern Ireland have still been produced meaning that there was a gap in evidence for Wales. However methodological differences limit how comparable the different measures across the UK are. Information on what is published in the rest of the UK is included in a later section.

Methodology

9. The HEIP measure is the sum of the initial participation rates for each age from 17 to 30 inclusive. The initial participation rates are the proportion of each age group that is participating in HE for the first time and to calculate this we need two pieces of information. The first piece of information is the number of students of each age who are participating in HE for the first time and the second is the overall population of that age in Wales.

Step 1: Estimating the number of students initially participating in HE

10. We use three data sources to estimate the number of students of each age who are participating in HE for the first time. These sources are the Higher Education Statistics Agency (HESA) Student Record, the HESA Student Alternative record (for the years 2014/15 to 2021/22) and the Lifelong Learning Wales Record (LLWR) from 2016/17 onwards collected by the Welsh Government.

11. For the HESA records we have linked data from 2004/05 to 2022/23 to identify when a person appears multiple times in the data. Details of this linking are provided in Annex A. With the records linked we find the earliest record for a student where they studied, or were still expected to study, for at least 6 months to ensure they have a considerable engagement in HE. We also check if they have previously obtained a HE level qualification and exclude those that have as they will have previously participated in HE.

12. For the LLWR data, we identify the first academic year where a student has either a learning programme or learning activity that is at the equivalent of  Level 4 or above in the Credit and Qualifications Framework for Wales (CQFW). As with the HESA  data we require that the relevant programme or activity lasts, or is expected to last, at least 6 months. Students who had a HE level qualification on entry are also excluded again.

13. The identification of students’ initial participation is performed separately for the HESA and LLWR data so the number of initial participants in each are combined to give the total number of initial participants in each academic year. The initial participants are divided into their age as at 31 August at the beginning of the academic year, e.g. for the 2022/23 academic year the students ages are calculated as at 31 August 2022.

Step 2: Estimating the overall population

14. Two data sources are used to estimate the population of Wales. These are the 2021 Census out-of-term population estimate and the mid-year population estimates for Wales from 2016 to 2022, both produced by the Office for National Statistics (ONS).

15. The base for the population estimate is the 2021 Census out-of-term population estimates. The out-of-term population is produced by the ONS as part of their census outputs, and is the usual resident population but with full-time schoolchildren and students counted at their out-of-term address. This population has been used as the base rather than using the mid-year estimates directly as we want to count students where they usually live rather than where they are studying.

16. The out-of-term population is based on the 2021 Census day of 21 March 2021 so an adjustment is made to age the population to 31 August 2021 to match the date used in the student data. For example, the number of 18 year olds is estimated as being a proportion of 17 year olds who have turned 18 since 21 March and the proportion of 18 year olds who have not yet turned 19 since 21 March.

17. A similar adjustment is made to the mid-year population estimates to age these populations from 30 June, the date of the mid-year estimates, to 31 August. We then calculate the percentage change between each adjusted mid-year estimate and the adjusted 2021 mid-year estimate for each age. These percentage changes are applied to the adjusted out-of-term population to produce at out of term-time population estimate as at 31 August for each year.

Step 3: Calculation of the initial participation rates and the HEIP measure

18. For each age from 17 to 30 years old we calculate the initial participation rate for that age by dividing the number of initial participants of that age from step 1 by the estimated population of that age from step 2.

19. The HEIP measure is calculated by summing the initial participation rates for each age. The idea behind this is that each individual participation rate represents the probability that someone of that age will participate in HE for the first time and by summing these you are building the probability that someone will participate in HE between the ages of 17 and 30 years old if these probabilities remain the same.

20. For clarity, the HEIP is not the same as dividing the total number of initial participants aged 17 to 30 in an academic year by the overall Welsh population of those ages. This would produce a much lower figure and would assume that someone is equally likely to be an initial participant in HE at any age, which is not the case.

Limitations

21. There are several limitations to note regarding the calculation of the HEIP used in this report.
a.) Initial participation in HE through studies that are not collected in the HESA records or LLWR are not be included in this measure. This would include any Welsh domiciled students studying at HE level at further education colleges in England, Scotland and Northern Ireland, some independent HE institutions in the UK or at higher education providers outside of the UK.
If someone obtained a HE level qualification via the above routes then any further HE studies that were recorded in the HESA or LLWR data would also not be included in the measure as they would be excluded due having a HE level qualification on entry.
This issue could be reduced by obtaining additional data sources that cover these other options for HE level studies.
b.) As the HESA and LLWR data are not linked together, it would be possible for someone to appear as an initial participant in both if they participated but did not obtain a HE level qualification. For example someone could appear in the HESA data but drop out after one year without obtaining any qualifications. They could then appear in the LLWR data and still be considered an initial participant. This issue would be reduced by linking the datasets prior to looking for initial participants.
c.) The measure assumes that the initial participation rates for each age will continue, however it doesn’t account for differences in participation between cohorts. For example, the levels of participation of the cohort of 18 year olds in 2022/23 when they reach the age of 30 may differ from what those who are 30 in 2022/23 for a variety of reasons including policy changes and the wider economic landscape.
d.) While the population estimates used for the overall populations are all accredited official statistics, there have been a number of assumptions made to adjust these to fit the purposes of this measure.
The first adjustment is to age the estimates to 31 August so the age is comparable to the age used from the student data and the age is relevant to the academic years. However, this adjustment uses the assumption that birth dates are equally distributed which is not the case.
The second adjustment is to ‘grow’ the adjusted out of term-time population to create a time series based on the percentage changes seen in the adjusted mid-year estimates. This assumes that the mid-year population and the out-of-term population change at the same rate.
e.) Domicile is not static. This means we are not following a specific group of people and estimating how many of them participate in HE. Instead the population that is being considered is always changing and the population is affected by inward and outward migration.
For example, someone could live in Wales until they are 24 before moving to England, if this person then participated in HE for the first time at the age of 25 they would not be included in the calculation as they would be English domiciled at that point. Conversely, someone living in England before moving to Wales and then participating in HE for the first time would be included.
f.) As this methodology does not follow specific cohorts of people, it is difficult to produce reliable figures on more detailed characteristics. This is particularly difficult if characteristics change over time, for example whether someone lives in a more deprived area, or it is difficult to get accurate population estimates.
g.) In the absence of a universal identifier to link records, algorithms are used to link the HESA student records and this will mean some incorrect links are made, or real links may be missed. In the case of incorrect links being made, then an individual’s initial participation could be discounted as we will believe they have participated in HE previously. In the case of a real link being missed then an individual could be counted as an initial participant twice, although this should be minimised by excluding those who have a prior HE level qualification recorded in the data.
These incorrect or missed links can occur due to data quality issues, such as incorrect information being recorded or digits being swapped in dates of birth. They can also occur when someone’s data is correct but varies over time, for example using different variations of their name or if someone changes their name.

Results

22. The HEIP measure is the sum of the initial participation rates for each age from 17 to 30 years old in a given academic year. The HEIP measure is not the percentage of 17 to 30 year olds who are participating in HE in that particular year. Instead the HEIP measure is an estimate of the probability that a Welsh domiciled person will participate in HE by the time they are 30 based on the initial participation rates in that year.

23. The HEIP measure in 2022/23 was 54.6%. This means that the estimated probability of a Welsh domiciled person participating in HE by the age of 30 is 54.6% based on the initial participation rates for each age from 17 to 30 in 2022/23.

24. After a drop between 2016/17 and 2017/18, the HEIP measure increased every year from 2017/18 to 2020/21 reaching a high of 58.9%. From this peak in 2020/21 there has been a decline in the following two years down to the figure of 54.6% in 2022/23. The Covid-19 pandemic will have been a factor on the levels of participation in the most recent years.

By Age

Table 1: Initial entry percentages by age – 2016/17 to 2022/23

Age	2016/17	2017/18	2018/19	2019/20	2020/21	2021/22	2022/23
17	0.8%	0.4%	0.5%	0.6%	0.3%	0.2%	0.2%
18	27.5%	27.4%	27.0%	28.1%	28.4%	29.5%	29.6%
19	10.4%	10.1%	10.3%	10.7%	11.0%	10.2%	9.4%
20	3.4%	3.5%	3.5%	3.5%	3.8%	3.4%	3.3%
21	1.9%	1.9%	2.2%	2.1%	2.4%	2.2%	1.8%
22	1.4%	1.5%	1.6%	1.6%	2.0%	1.7%	1.6%
23	1.2%	1.1%	1.3%	1.5%	1.7%	1.5%	1.3%
24	1.2%	1.2%	1.3%	1.4%	1.6%	1.3%	1.2%
25	1.1%	1.1%	1.2%	1.3%	1.5%	1.3%	1.1%
26	1.0%	1.0%	1.2%	1.3%	1.4%	1.2%	1.0%
27	1.0%	1.0%	1.1%	1.3%	1.3%	1.2%	1.2%
28	1.0%	0.9%	1.1%	1.1%	1.3%	1.1%	1.0%
29	1.1%	0.9%	0.9%	1.1%	1.2%	1.1%	0.9%
30	0.9%	0.8%	1.0%	1.1%	1.2%	1.0%	1.0%
HEIP measure	53.9%	52.8%	54.2%	56.6%	58.9%	56.8%	54.6%

25. The largest contribution to the HEIP measure comes from 18 and 19 year olds. In 2022/23 the initial participation rates for these two ages contribute 38.9 percentage points to the overall HEIP measure of 54.6%.

26. The initial participation rate of 18 year olds has increased in every year since 2018/19.

27. For other ages the initial participation rates generally increased between 2017/18 to 2020/21, before falling in the following two years.

By Sex

28. As with the overall HEIP measure, the HEIP measure for males and females decreased between 2016/17 and 2017/18 before increasing every year until 2020/21. There was then a drop in each of the following two years.

29. The HEIP measure is considerably higher for females than for males, with the gap widening across the period. In 2016/17 there was a difference of 16.5 percentage points while in 2022/23 there was a 21.6 percentage point difference.

30. The HEIP measure for females peaked at 69.8% in 2020/21 compared to 48.3% for males in the same year. The HEIP has since dropped to 65.3% and 43.7% for females and males respectively in 2022/23.

By Age and Sex

Age	Females 2021/22	Females 2022/23	Males 2021/22	Males 2022/23
17	0.2%	0.3%	0.1%	0.2%
18	35.9%	35.1%	23.5%	24.0%
19	11.8%	11.4%	8.5%	7.4%
20	3.9%	3.7%	2.9%	2.8%
21	2.4%	2.1%	2.0%	1.5%
22	2.0%	1.8%	1.4%	1.3%
23	1.9%	1.6%	1.1%	1.0%
24	1.7%	1.5%	1.0%	0.9%
25	1.6%	1.4%	1.0%	0.8%
26	1.4%	1.4%	0.9%	0.7%
27	1.4%	1.4%	0.9%	1.1%
28	1.3%	1.3%	0.8%	0.7%
29	1.4%	1.1%	0.9%	0.7%
30	1.2%	1.2%	0.8%	0.8%
HEIP measure	68.0%	65.3%	45.8%	43.7%

31. Table 2 shows that the initial participation rates are higher for females than males at every age from 17 to 30 years old for 2021/22 and 2022/23. This is also the case when looking back to 2016/17, with the exception of 17 year olds between 2017/18 and 2019/20 where the rates were level.

32. The overall initial participation rate for 18 year olds increased between 2021/22 and 2022/23, however when looking at the measure by sex this was only the case for males. The initial participation rate for female 18 year olds dropped by 0.8 percentage points between 2021/22 and 2022/23 while there was a 0.5 percentage point increase for male 18 year olds.

Participation measures in the rest of the UK

33. There is no single measure of participation across the UK making difficult to make comparisons. This section covers the differences and similarities in other participation measures across the UK.

England

34. The Department for Education (DfE) have a statistical series called ‘Participation measures in higher education’. The methodology for this series was similar to what has been used here up to the academic year 2019/20 release of the DfE statistics.

35. A new methodology called the Cohort-based Higher Education Participation (CHEP) measure was introduced for the 2020/21 academic year. Instead of estimating future participation by age 30 using current participation levels as the HEIP methodology does, the CHEP tracks cohorts of school pupils to measure participation.

36. While CHEP is quite different from the HEIP methodology, the 2021/22 release does have a section ‘Projecting future HE participation’ that uses the cohort data to produce a projection that is more similar to how the HEIP measure is constructed.

37. The rationale for changing methodology was that while the HEIP produced a timely measure there were some known limitations such as:

• estimating a higher participation rate than the real rate for a particular entry cohort when there is steady growth in entry rates for younger age groups.
• not being able to create reliable figures by region and key demographics.

38. DfE felt the CHEP methodology lessened the impact of inward and outward migration flows over time and that it would also not be affected by revisions to the ONS population estimates that occur following each Census.

39. The other benefit was that the CHEP approach would allow them to analyse participation by pupil characteristics taken from the school census such as breakdowns by gender and region of school attended.

40. One drawback of the new methodology is that it is less timely than the HEIP as it requires each 15-year-old school cohort to reach a particular age before reporting on it. In other words you would only report on the percentage participating in HE by the age of 25 for those aged 15 in the 2024/25 academic year, once the 2034/35 academic year data are available.

Scotland

41. The Scottish Funding Council (SFC) include a Higher Education Initial Participation Rate (HEIPR) in the background tables of their ‘HE Students and Qualifiers at Scottish Institutions’ statistical publication.

42. This is produced using a similar methodology to what has been presented for Wales in this publication. Although there will be differences in the exact methodology for how initial participation is identified. One difference is that it covers those aged 16 to 30 rather than 17 to 30.

43. One similarity to note is that the HEIPR for Scotland also reaches a peak in 2020/21. However unlike the HEIP for Wales, after falling in 2021/22 it then increased again in 2022/23.

Northern Ireland

44. The Northern Ireland Statistics and Research Agency (NISRA) have produced an ‘Age Participation Index for Northern Ireland’ for 1998/99 to 2021/22. This is the number of Northern Irish domiciled young entrants (aged under 21) to full-time Higher Education in the UK or Republic of Ireland as a percentage of the 18-year-old population in Northern Ireland.

Future developments

45. Any feedback received will help direct how the HEIP could be improved. Developments will be informed by the discussions we have with those with an interest in this area, but possible developments include:

• Extending the coverage of HE activity by obtaining data on Welsh domiciled initial participants studying at HE level in Further Education Providers in England, Scotland and Northern Ireland.
• Investigating whether it would be possible to robustly report on a wider range of characteristics. For example ethnicity, disability and living in more deprived areas.
• Investigate whether there are possibilities to produce initial participation rates using a cohort based methodology as the DfE do for England. When the work on this HEIP was started in the Higher Education Funding Council (HEFCW) a cohort methodology was not feasible due to the lack of availability of longitudinal data. However the establishment of Medr may provide new opportunities.
• Look at how the measure could be adapted for the wider tertiary education sector instead of only focusing on HE.

Summary

This analysis builds on that previously published as part of the Welsh Government statistical article ‘outcomes for learners in post-16 education affected by the coronavirus (Covid-19) pandemic: August 2020 to July 2021‘. It aims to provide an up to date picture of progression from Year 11 to tertiary education.

The Year 11 cohorts in this analysis are based on all learners enrolled in Year 11 in maintained secondary, middle and special schools in Wales.

The tertiary education destinations considered in this analysis are publicly funded provision in maintained school sixth forms, further education colleges (excluding adult community learning) and work-based learning providers in Wales. Also included is post-16 learning in maintained special schools and the Welsh Government’s Jobs Growth Wales+ / Traineeships employability programmes.

The analysis does not include tertiary destinations in independent schools, other independent or specialist learning providers, tertiary education outside of Wales or any other post-16 EOTAS (Educated Other Than at School) provision.

Main points

• The provisional proportion of learners progressing from Year 11 to tertiary education was 90% in 2024/25, unchanged from the previous three years.
• The number of learners progressing has increased steadily since 2018/19.
• Of the learners that progressed from Year 11 to tertiary education:
  • An increasing proportion are progressing to further education colleges, with a corresponding decrease in those progressing to sixth forms.
  • There have been recent decreases in the proportion of learners studying at level 3 (including AS levels).
• There are differences in progression between different groups of learners. The proportion progressing was higher for learners who are:
  • Female
  • Living in the least deprived neighbourhoods
  • Not eligible for Free School Meals
  • Not accessing special educational needs or additional learning needs provision
  • From Asian, Asian British, Asian Welsh ethnic groups
  • Competent or fluent in their acquisition of English as an additional language
  • Attending Welsh medium schools in Year 11, or are fluent in Welsh.
• There was substantial variation in the type and level of tertiary education provision between different groups of learners and geographically.

You can find the full data in the PDF Sta/Medr/04/2025.

Quality and methodology

Data sources

The data sources used in this release are:

• Pupil Level Annual School Census (PLASC): an electronic collection of pupil and school level data provided to Welsh Government by all maintained sector primary, middle, secondary, nursery and special schools. The data collected is based on a January census date.
• Post-16 Data Collection: every autumn, all maintained schools with sixth forms are required to report all learning programmes and activities undertaken by pupils in the previous academic year.
• Lifelong Learning Wales Record (LLWR): data on further education, work-based learning and adult community learning. It’s collected on a ‘rolling’ basis throughout the year with regular statistical freezes. It is the official source of statistics in Wales for these sectors.
• School attendance weekly management information data collection: weekly data extracted directly from school management information systems started in Autumn 2020. The data is collected from all maintained nursery, primary, middle, secondary and special schools and any pupil referral units that have such management information systems and routinely record their information electronically.

Methodology

The main changes to the methodology used in the analysis previously published in the Welsh Government statistical article ‘outcomes for learners in post-16 education affected by the coronavirus (Covid-19) pandemic: August 2020 to July 2021‘ are:

• For academic years 2022/23 and previous, final datasets are used to identify tertiary education destinations rather than in-year datasets subject to further change.
• The Post-16 Data Collection is used for sixth form destinations as far as possible, providing information on the type of learning programme being studied. Weekly management information on school attendance is used for 2023/24 and 2024/25 as the Post-16 Data Collection is not yet available for these years.
• Post-16 learning destinations in maintained special schools are reported.

Year 11 cohorts are defined as any learner on roll in a maintained secondary, middle or special school in Wales on the PLASC census date.

For academic years 2017/18 to 2022/23, the Post-16 Data Collection and LLWR are used to identify tertiary education programmes of study that were active at any point during the year. Programmes included in this analysis include the following publicly funded learning:

• Any programme of study in school sixth forms.
• Further education undertaken in further education colleges.
• Work-based learning, either undertaken in further education colleges or private training providers including apprenticeships, Jobs Growth Wales+ and traineeships.

PLASC data is also used to identify any learners undertaking post-16 provision in maintained special schools.

For academic years 2023/24 and 2024/25, LLWR data is used as above. The Post-16 Data Collection is not currently available for these years, therefore weekly management information on school attendance is used to identify learners in school sixth forms and undertaking post-16 provision in maintained special schools. There are a number of limitations as a result of using this management information.

Based on comparisons for the 2021/22 and 2022/23 academic years, the weekly management information on school attendance over-estimates overall progression by around half a percentage point compared to the Post-16 Data Collection. It also causes over-estimates in the proportion of learners switching their tertiary education programme and leaving their tertiary education programme without completing it.

Of the learners who progressed, the proportions attending sixth forms are over-estimated by between 1.5 and 3 percentage points, with an under-estimate in proportions attending FE colleges.

Year 11 cohorts are then linked to the various datasets containing information on tertiary education – initially on the Unique Pupil Number and Unique Learner Numbers, with further linkage on unmatched records based on names and dates of birth.

Limitations

Figures for 2024/25 are provisional as they are based on in-year data. Tertiary programmes of study have been drawn from the January 2025 LLWR freeze. The data may not fully reflect all learning up to the point the freeze was taken and will be subject to change in the future. Data for the remainder of the academic year is not included, which may affect statistics for 2024/25. A relatively small number of learners may start their first tertiary programme of study after January, most commonly in work-based learning.

For 2024/25, weekly management information on school attendance is available up to the end of the winter term.

Figures for 2023/24 are also provisional as the Post-16 Data Collection will replace the weekly management information on school attendance once it is available.

The weekly management information on school attendance has not undergone the same level of quality assurance as accredited official statistics and the data may be subject to future revisions. It does not provide any information on the learner’s programme of study.

This analysis does not include tertiary education destinations outside of Wales, or any independent or specialist tertiary education. The proportion of learners progressing from Year 11 to tertiary education in local authorities that border England (Flintshire, Wrexham, Powys, Monmouthshire) is likely to be affected.

Definitions

The tertiary education destinations reported in this analysis are based on the first programme of study undertaken by the learner. When identifying a learner’s first programme the following programmes are prioritised over other FE programmes: AS level, A2 level, vocational, apprenticeships and Jobs Growth Wales+/traineeships. The most common programmes they’re prioritised over are GCSEs which are often taken as supplemental courses.

Only enrolment into the academic year immediately following Year 11 is included. Learners who started tertiary education in a later academic year are not included in this analysis.

Where weekly management information on school attendance is used, learners are considered to be still enrolled in tertiary education if they have an attendance or authorised absence record within 2 weeks of the following dates:

• 31May 2024 for the 2023/24 academic year, as the attendance data becomes more unreliable during the summer examination period.
• 20 December 2024 for the 2024/25 academic year, the final data of the winter term as data for only part of the academic year is available.

The school attendance dataset is taken directly from schools’ Management Information Systems. In some cases learners appear to be automatically rolled over from Year 11 into Year 12 when this was not the case. Because of this a learner is not listed as enrolled if they were:

• not listed as enrolled after 6 September,
• and had not attended the school or had a specific recognised absence before 6 September,
• and it was the same school that they were enrolled into in Year 11.

All analysis by characteristics are based on those recorded for the learner as part of their Year 11 PLASC record.

The deprivation decile of the learner’s home neighbourhood is based on the main index of the Welsh Index of Multiple Deprivation 2019.

‘AS level’ tertiary destinations here include both AS level and AS level equivalent programmes. AS level equivalent programmes consist of a mixture of AS levels and vocational qualifications, for example 2 AS levels and a BTEC National Certificate.

Rounding and suppression

All figures are rounded to the nearest 5. Numbers less than 5 are suppressed. Percentages are rounded to the nearest whole number. Percentages based on a denominator of less than 23 are suppressed.

Differences between values are calculated using unrounded values, so there may be small discrepancies when compared with the rounded figures.

Statement of compliance with the Code of Practice for Statistics

Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

All of our statistics are produced and published in accordance with a number of statements and protocols to enhance trustworthiness, quality and value. These are set out in our Statement of Compliance with the Code of Practice for Statistics. You are welcome to contact us directly with any comments about how we meet these standards.

Alternatively, you can contact OSR by emailing [email protected] or via the OSR website.

Trustworthiness

These statistics have been published according to our Statement of Compliance with the Code of Practice for Statistics and pre-release access to official statistics policy.

Quality

The statistics in this release have largely been produced from final versions of recognised administrative data sources used to produce official statistics on education in Wales. These have been supplemented with weekly management information on school attendance to provide the most recent estimates of progression from Year 11 to tertiary education (for the 2023/24 and 2024/25 academic years). The limitations of using this management information have been explained and these estimates are marked as provisional.

Value

These official statistics in development aim to comply with the Code as far as possible. They have been produced rapidly in response to demands for better analysis on participation in tertiary education in Wales.

They are labelled as ‘official statistics in development’ to test whether they meet user needs and to reflect that the methodology is not fixed and could be further developed based on user feedback. We would welcome any comment on the usefulness of these statistics. Please contact [email protected].

Key points:

• There were 4,380 apprenticeship learning programmes started in 2023/24 Q4, compared with 5,715 starts in 2022/23 Q4.
• Foundation Apprenticeships and Higher Apprenticeships saw the largest falls compared to Q4 the previous year.
• Healthcare and Public Services apprenticeships were the most popular sector in 2023/24 Q4 with 2,005 programmes started. This accounted for 46% of all apprenticeship learning programmes started.
• 60% of apprenticeship learning programmes started were by female learners in Q4 2023/24, a five percentage point decrease from 2022/23 Q4.
• 41% of apprenticeship learning programmes started were by learners aged 25 to 39 in Q4 2023/24, compared to 42% in Q4 for the previous year.
• 14% of apprenticeship learning programmes started were by learners with ethnic minority backgrounds in Q4 2023/24, this is unchanged compared to Q4 for the previous year.
• 12% of apprenticeship learning programmes started in Q4 2023/24 were by learners identifying as having a disability and/or learning difficulty, compared to 11% in Q4 for the previous year.
• There have been 63,410 apprenticeship starts since Q4 2020/21, as part of progress towards Welsh Government’s target of 100,000 apprenticeships.
• The Programme for Government contained a target to create 125,000 all-age apprenticeships. During the Economy, Trade and Rural Affairs Committee meeting on 26 June 2024, the Cabinet Secretary for Economy, Energy and Welsh Language agreed a new target of 100,000 all-age apprenticeships to maintain the previous Senedd term’s target of 100,000.

Apprenticeship learning programmes started: interactive dashboard

Quality and Methodology

Provisional data

The statistics in this report are produced quarterly. Figures for the first three quarters in an academic year are provisional because they are based on earlier freezes of the Lifelong Learning Wales Record (LLWR). This data will continue to be updated until the final freeze in December after the end of the academic year.

The provisional figures for the year are finalised when quarter 4 (May to July) data are published in February/March each year, based on the December freeze.

Target measure starts

The statistics for the target measures use a more rigorous measure of apprenticeship programme starts than other statistics in this output. This measure takes account of early drop outs (within first 8 weeks) and transfers between apprenticeships.

Degree apprenticeships are now included in the current target measure. Degree apprenticeships in Wales provide the opportunity to combine working with part-time study at university. Data is sourced from the Higher Education Statistics Agency (HESA). Whilst statistics from HESA have been calculated to be as comparable as possible with statistics for other apprenticeship programmes sourced from the LLWR (for example, removing early drop outs), some methodological differences will remain. Unlike the LLWR, HESA data is only available annually and statistics for the latest available academic year will be included in every Q4 update.

More quality information

Other than the provisional data and the target measure, these statistics are produced in the same way as the statistics in the Further education, work-based learning and community learning annual reports. More information can be found in the quality section of those reports.

Statement of Compliance with the Code of Practice for Statistics

Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

All of our statistics are produced and published in accordance with our Statement of Compliance with the Code of Practice for Statistics and other statistical policies.

These official statistics demonstrate the standards expected around trustworthiness, quality and public value in the following ways.

Trustworthiness

This is produced by professional statisticians complying to the Code of Practice for Statistics. Release dates are pre-announced, protocols around data confidentiality are followed.

Quality

The data is sourced from the Lifelong Learning Record Wales which is submitted by learning providers. This data is also used to determine funding for learning providers and is subject to audit.

When the data is submitted it must meet certain validation rules. When the statistics are being produced quality checks are undertaken by the statisticians.

Value

These statistics provide a quicker insight into the uptake of apprenticeships in Wales than the annually produced reports. They are used for monitoring and evaluating the sector. They report the progress against a target set by Welsh Government.

Contact: [email protected]

Date: Monday, 3 March 2025 – 14.00-15.00 (Online webinar)

Webinar slides

Webinar Q&A

About REF 2029

The Research Excellence Framework (REF) is how the quality of research is assessed in higher education institutions in the UK. It’s a process of expert review, with panels of experts in individual academic subject areas assessing institutions’ research submissions. The next REF will report in 2029.

The Learned Society of Wales is the national academy for arts and sciences.

Our Fellowship brings together experts from across all academic fields and beyond. We use this collective knowledge to promote research, inspire learning, and provide independent policy advice.

Universities Wales  is a membership body representing the interests of Wales’ nine universities.

We develop higher education policy, deliver political and stakeholder engagement, and campaign on issues where our members have a shared interest.

Key Points

Students

• The proportion of students with a disability has increased every year from 2016/17 to 2022/23. The proportion has increased from 13% in 2016/17 to 17% in 2022/23.
• The proportion of students with an ethnic minority background has increased in every year from 2016/17 to 2022/23. The proportion has increased from 10% in 2016/17 to 14% in 2022/23.
• The majority of students are female. This size of this majority has increased from 55% in 2016/17 to 57% in 2022/23.

Staff

• The proportion of staff with a disability increased each year from 2016/17 to 2022/23. For academic staff the proportion increased from 4% to 7% and for non-academic staff the proportion increased from 6% to 10%.
• The proportion of staff with an ethnic minority background has increased each year from 2016/17 to 2022/23. For academic staff the proportion increased from 11% to 17% and for non-academic staff the proportion increased from 4% to 6%.
• The majority of academic staff are male, although the size of this majority has fallen slightly from 53% in 2016/17 to 52% in 2022/23. The majority of non-academic staff in this period were female. The size of this majority is 62% in 2022/23 which is the same as in 2016/17.

Methodology information

Data Sources

The data for this release come from the Higher Education Statistics Agency (HESA) Student and Staff records collected by Jisc.

In 2022/23 the student data was collected with the revised data collection delivered by the Data Futures programme. Jisc conducted a comprehensive quality assessment on the dataset and this is detailed in their 2022/23 student data quality report. A summary of the Student data collection process for 2022/23 covering timescales, validation and business rules and checking processes is included on the HESA website. Information about the earlier years of student data can also be found on the HESA website.

A summary of the Staff data collection process and associated quality rules can be found on the HESA Staff data collection page.

Coverage – Students

The statistics include students who are part of HESA’s higher education standard registration population. More information on this population can be found in the student definitions on the HESA website.

All uses of ‘students’ in this bulletin refer to ‘student enrolments’. This is a count of each enrolment for a course. In rare instances where a student was enrolled in two different courses in the same year, that student would be counted twice.

Coverage – Staff

These statistics include staff who are in the HESA staff contract population, which includes those individuals who have one or more contracts (which are not atypical) that are active on 1 December in the relevant HESA reporting period. Staff on a atypical contract are those members of staff whose contracts involve working arrangements that are not permanent, involve complex employment relationships and/or involve work away from the supervision of the normal work provider.

All figures on staff are the full-person equivalents (FPE). Individuals can hold more than one contract with a provider and each contract may involve more than one activity. In analyses staff counts have been divided amongst the activities in proportion to the declared full-time equivalent for each activity. This results in counts of FPE.

More information on this population can be found in the staff definitions on the HESA website.

Rounding Strategy

The data presented in this report follow the principles of the HESA Standard Rounding Methodology. The strategy is intended to prevent the disclosure of personal information about any individual.

This means that:

• Student and staff counts are rounded to the nearest multiple of 5.
• Percentages are calculated based on the unrounded counts and exclude unknowns. Percentages are not published if they are fractions of a small group of people (fewer than 22.5).
• Totals are also subject to this rounding methodology. As a result, the sum of numbers in each row or column may not match the total shown precisely.

Quality information

Statement of compliance with the Code of Practice for Statistics

Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

All of our statistics are produced and published in accordance with a number of statements and protocols to enhance trustworthiness, quality and value. These are set out in Medr’s Statement of Compliance with the Code of Practice for Statistics.

These official statistics demonstrate the standards expected around trustworthiness, quality and value in the following ways.

Trustworthiness

These statistics have been published according to Medr’s Statement of Compliance and pre-release access to official statistics policy.

Quality and Value

This section provides a summary of information on this statistical release against five dimensions of quality: Relevance, Accuracy, Timeliness and Punctuality, Accessibility and Clarity, and Comparability and Coherence. These also cover the aspects of the Value pillar in the Code of Practice for Statistics.

1. Relevance
   The data in this report gives an overview of some equalities characteristics of students and staff in the higher education sector in Wales. This can be used to identify how effective particular policies related to equalities characteristics in higher education are, or to identify whether those with particular characteristics are under-represented in higher education.
   
2. Accuracy
   The HESA student and staff data are both censuses rather than surveys, as such there is no inaccuracy due to estimation. However, the accuracy of the data can be affected by errors in the data submitted. This is mitigated with a comprehensive set of quality checks, where potential issues are queried with providers so a suitable explanation for the data can be reached, or the data is corrected if necessary.
   The other factor affecting accuracy is where personal characteristics are returned as unknown. During the data collection process high levels of unknown values are queried with HE providers to minimise this where possible. The number of students and staff returned with unknown values are included in the spreadsheet and PowerBI dashboard so the scale of these are clear to users.
   
3. Timeliness and punctuality
   The data in this release refers to student and staff data up to the 2022/23 academic year. As the HESA student and staff data collections are retrospective collections there is a lag between the academic year and when the data can be made available. This lag has been extended for this publication due to two factors:
   * Delays to the student data collection as a result of the implementation of the Data Futures programme. This resulted in data being available later than usual.
   *The establishment of Medr. Prior to this release, these statistics were published by HEFCW. Unlike HEFCW, Medr is a producer of Official Statistics and setting up the appropriate processes for this, as well as the general establishment of Medr, contributed to an increased amount of time required to produce this analysis.
   The latter of these factors will not affect future versions of this release, and the delays associated with the Data Futures programme will reduce as the new data collection process becomes established.
   
4. Accessibility and clarity
   This statistical release was pre-announced on the Welsh Government’s statistical release calendar.
   This report is accompanied by a PowerBI dashboard and a spreadsheet which can both be accessed on the Medr website.
   
5. Comparability and coherence
   As the HESA student and staff data collections are UK-wide data collections, these statistics can be compared to similar analysis of equalities data for Higher Education providers across the UK which is available on the HESA Open Data website.

Introduction

This publication provides information about staff employed at higher education institutions in Wales as collected in the Higher Education Statistics Agency (HESA) Staff Record. Previous versions of this series were published by the Welsh Government and can be found on the Welsh Government website.

Main points

• Overall, there has been a 4% rise in the number of staff at Welsh universities from 21,815 in 2022/23 to 22,635 in 2023/24.
• Staff numbers were higher in 2023/24 than they were in 2022/23 for Cardiff University (10%), Wrexham University (9%), University of Wales Trinity Saint David (9%), Cardiff Metropolitan University (5%) and University of South Wales (4%).
• Staff numbers were lower in 2023/24 than they were in 2022/23 for Swansea University (1%), Aberystwyth University (4%) and Bangor University (8%).
• Cardiff University employed the most staff (7,760) followed by Swansea University (3,825).
• Wrexham University was the smallest university in terms of staff numbers, employing 585 staff in 2023/24.
• Staff are evenly split between academic and non-academic contracts across the sector, both accounting for 50% of all staff.
• 60% of academic contracts were full-time and 74% of non-academic contracts were full-time.
• Of those on non-academic contracts 5,130 (45%) were in professional or technical occupations, 3,745 (33%) were in administrative and secretarial occupations, 1,055 (9%) were managers, directors or senior officials and 735 (7%) were in elementary occupations. The definitions of these occupation groups come from the nine Major Groups of the Standard Occupational Classification (SOC) 2020.
• 56% of staff across the sector were female. However, only 49% of academic contracts were held by female staff. Two thirds of all part-time staff were female (65%).
• 9% of academic teaching staff reported that they were able to teach through Welsh and of those, 64%^(r) were known to be teaching in Welsh.

Staff numbers are calculated using the full-person equivalent for staff at 1 December of the reporting year. Staff on atypical contracts are not included. Atypical staff are those members of staff whose contracts involve working arrangements that are not permanent, involve complex employment relationships and/or involve work away from the supervision of the normal work provider.

^(r) Revised on 01 May 2025. After publication an error with the data submitted by Bangor University was identified and this figure has been updated to reflect the corrected data.

Data

The data is available on StatsWales and HESA Open Data.

Quality and methodology information

Figures are based on the Higher Education Statistics Agency (HESA) Staff Record. For Welsh institutions submitting to the staff record data is required for all academic staff, and for non-academic staff if the contract is not atypical. Data also need not be returned for agency staff, self-employed staff, honorary contracts where the contract is not deemed to be a contract of employment and staff not employed by the HEI, but by a company consolidated into the HEI’s accounts.

Non-atypical staff full-person equivalent (FPE) counts are calculated on the basis of contract activities that were active on 1 December of the reporting period.  Atypical staff FPE counts are calculated on the basis of those individuals who have only atypical contracts that were active during the reporting period.

More information related to definitions used can be found at www.hesa.ac.uk/support/definitions/staff.

Statement of Compliance with the Code of Practice for Statistics

Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

All of our statistics are produced and published in accordance with our Statement of Compliance with the Code of Practice for Statistics and other statistical policies.

These official statistics demonstrate the standards expected around trustworthiness, quality and public value in the following ways.

Trustworthiness

This is produced by professional statisticians complying to the Code of Practice for Statistics. Release dates are pre-announced, protocols around data confidentiality are followed.

Quality

The data is sourced from the HESA Staff Record which collects data from higher education providers across the UK. When the data is submitted it is checked against various quality rules with further quality checks undertaken by analysts producing analysis.

Value

These statistics provide information on the staff working at higher education institutions in Wales.

Contact

Email: [email protected]

Introduction

1. This publication provides guidance to the internal auditors of higher education institutions (HEIs) and further education institutions (FEIs) funded by Medr for higher education provision referred to throughout as higher education providers (HEPs) to use for their annual internal audit of the internal controls relating to the systems and processes in place to produce higher education (HE) data returns, and requests a copy of this internal audit report for each HEP.  Both HEFCW and Medr are referenced throughout this publication depending on historic or current data and processes.

2. Previously, external audits were commissioned by HEFCW so that HEPs were externally audited at least once every four years. 2021/22 was the last year of the contract HEFCW had with external auditors to do this and so in Medr we are continuing with the interim process used last year in place of external audits until the audit process is reviewed.

3. For 2025 the process will involve members of the Medr Statistics team meeting with data contacts at each HEP, to cover items such as previous audit findings, Data Futures implementation and review, and data quality. As part of this interim process, Medr will continue to rely on the annual assurance provided to HEPs and their Audit Committees by their internal auditors about the systems and processes used to produce data returns. Relying on the internal audits will maintain an adequate level of annual assurance in respect of HEPs’ data returns.

4. The internal audit will provide an opinion as to the adequacy and effectiveness of the controls in place to manage the risks relating to the accuracy of data submitted by the HEP to the Higher Education Statistics Agency (HESA), Medr and Welsh Government (WG), including data used in calculations for the following funding streams:

• Teaching funding (currently comprising per capita and premium funding and part-time (PT) undergraduate (UG) credit-based funding);
• Research funding comprising Quality research (QR) funding and Postgraduate research (PGR) training funding;
• Research Wales Innovation Funding (RWIF);
• Medr’s part-time undergraduate fee waiver scheme;
• Well-being and mental health funding;
• Race access and success funding;
• Targeted employability support funding;
• Wales Research Environment and Culture (WREC) funding;
• Capital funding.

and the data used to monitor the following funding streams:

• Medr’s part-time undergraduate fee waiver scheme;
• PGT Master’s bursaries allocations;
• Medr funded Degree Apprenticeship scheme allocations.

5. The internal audit should also provide assurance over the controls in place to ensure the accuracy of data used in the monitoring of performance, including key performance indicators such as the National Measures, and if applicable, data included by HEPs as part of the fee and access plan reporting requirements.

6. The Data Futures programme was implemented for the 2022/23 HESA student record. There were difficulties with the return caused by delays to the functionality of the HESA Data Platform, late software updates, late supply of data quality rules by Jisc and other issues in its implementation year.  In light of this, for the 2024 audit scope we didn’t recommend that auditors examine the implementation of the new record for 2022/23 in depth, or the systems and process relating to the 2022/23 return, but rather provide opinions on the controls in place to manage risks relating to the record going forward including plans to review and/or improve processes, documentation and data quality moving into the 2023/24 return. Difficulties were also experienced in returning the 2023/24 student record and this may mean that providers have not been able to fully implement new processes and procedures for their systems and auditors should take these difficulties into account when setting out their programmes of work for 2025. We would expect auditors to include in the scope any updates applied to systems and processes, and to risk registers, after review of both the 2022/23 and 2023/24 student data returns.

7. This document provides guidance to the internal auditors about the nature of the controls that their audit should address, to assess whether the systems and processes are adequate to provide accurate data returns and data to use in funding and monitoring and also to ensure that internal audits taking place across the sector are carried out on a consistent basis.

8. If the internal audit report’s overall conclusion, or the conclusions relating to the adequacy of the design of the methods of control and the application of those controls, provides a negative opinion (e.g. limited or no assurance, unsatisfactory or inadequate controls) and/or the report includes a significant number of recommendations, Medr should be notified as soon as the opinion has been agreed. Medr will then conduct their own assessment of the issue and/or commission their own external audit as appropriate. This external audit would consider the accuracy of data for the current period and also consider the findings of the internal auditor and aim to assess the extent of potential errors in the data returns and data used for funding and monitoring for prior periods up to the last external audit. The findings of this external audit may result in adjustments to funding and further action may be taken if HEPs are found to be not compliant with their fee and access plans, the supply-side code of practice for data collections or the financial management code.

Scope of the Audit

9. The way in which internal audit work and controls testing is carried out at each HEP will depend on the systems and controls in place and how information is shared within the HEP. However, it is expected that the internal audit work will cover the elements highlighted in this document. Where previous internal audit work has found that the systems and controls in place are satisfactory, it may be considered appropriate by the HEP’s Audit Committee for subsequent audits to only cover areas of risk. In particular, due to the increased risks associated with the implementation of the HESA Data Futures programme in 2022/23 and into 2023/24 collection, we would expect to see this area of work included in the scope, (See also paragraph 62).

10. Auditors should ascertain the processes by which data returns and monitoring information are compiled and document them to the extent necessary to enable an evaluation to be made of the adequacy of the existing controls used by the HEP to ensure that they produce accurate data returns and appropriately compile monitoring data. Examples of the controls that the audit would normally be expected to assess are set out for all the current funding streams, data returns and other areas of audit in the sections below. Many of the controls are common to the data returns for all areas of audit. However, not all of the areas of audit apply to all HEPs, and auditors should refer to the relevant paragraphs.

11. Auditors should note that there are some areas where HEPs may have to return estimates, where information is not known at the time of return or information is not available in the required form. Estimates can be made using methods suggested by HEFCW/Medr in its guidance, or if appropriate, HEPs can use their own methods. Where estimates have been made, auditors should review the methods used to calculate them, confirm that they are properly documented, reasonable, consistently applied and tested for reliability.

12. If a HEP is in the process of merging or has recently merged with one or more other HEPs, the auditor should ascertain if procedures have been put in place to integrate their data systems or otherwise ensure that returns for the whole merged HEP can be made.

13. In planning the audit, the Auditor should consider the findings and conclusions of the latest external and/or internal audit reports relating to systems and data returns for the HEP and any follow up reports and correspondence with management to assess the extent of implementation of the reports’ recommendations. It is expected that the audit reports will make reference to and comment upon the extent that recommendations made by auditors in the previous internal or external audit reports have been effectively implemented.

14. Additionally any data issues or errors notified either directly to Medr by the provider, or identified and communicated by HEFCW/Medr, should be referenced in the report together with any action taken to ensure that data systems and processes have been amended where appropriate to mitigate against any such errors in future. As explained in paragraph 6, there were difficulties with the implementation of the Data Futures programme. This led to multiple errors being flagged and tolerated in the HESA student record issue management system (IMS) in both 2022/23 and 2023/24. We are not expecting auditors to review these errors, but would recommend any review for the HESA student record for the 2023/24 return focus instead on providers’ plans to review these errors and any action they might take to improve systems and processes moving into future HESA student record returns.

15. It is recommended that internal audit staff with some experience of the HE sector and associated data returns are involved in the visits to HEPs undertaken as part of the review and that auditors are sufficiently briefed on the guidance contained within this publication prior to carrying out the audit. In addition, auditors should make themselves aware of the UK-wide issues experienced with the implementation of Data Futures in 2022/23 and the issues experienced for the 2023/24 return. Advice and clarification relating to the guidance in this publication can be obtained from Medr via [email protected], and Medr staff are available to meet with internal audit staff if required.

16. All HEFCW/Medr publications described below are available via the relevant links in this publication or can be obtained from Medr directly via [email protected].

Funding Methodology and Data Requirements

17. HEFCW circular W24/13HE HEFCW’s Funding Allocations 2024/25 describes the overall funding distribution for academic year 2024/25 including:

• PGR and QR funding (pages 6&7)
• RWIF (page 7)
• Teaching funding (pages 8 to 11)

W24/13HE also includes funding which is further described in the following publications:

• Well-being and health strategy funding (Medr/2024/07)
• Part-time undergraduate fee waiver scheme (W24/15HE)
• Race equality in higher education allocations (Medr/2024/03)
• Targeted employability support for students (W23/15HE)

18. HEFCW circular W23/27HE Higher Education Data Requirements 2023/24 informs HE providers of the 2022/23 data used to calculate funding allocations and used for monitoring purposes, as well as student eligibility criteria for:

• Per capita funding (Annex A para 18)
• Access and retention premium (Annex A para 20)
• Disability premium (Annex A para 34)
• Welsh medium premium (Annex A para 36)
• Expensive subjects premium (Annex A para 41)
• Higher cost subjects premium (Annex A para 46)
• Part-time undergraduate fee waiver scheme (W24/15HE)
• Race equality in higher education allocations (Medr/2024/03)
• Targeted employability support for students (W23/15HE)

19. Medr publication Medr/2024/01 Higher Education Data Requirements 2024/25 informs HE providers of the data used to calculate funding allocations and used for monitoring purposes using 2023/24 HESA student record data.

20. Due to the implementation of HESA Data Futures, auditors should note the caveats included for 2022/23 and 2023/24 data, given the new nature of the data return, in paragraphs 3 and 4 of Medr/2024/01, and our expectations about audit of the systems and processes for the 2023/24 HESA student data return described in paragraphs 6 and 14 of this publication.

21. Annex A of this publication contains an outline of the methodology used to calculate the formula driven elements of credit based funding for teaching, RWIF, PGR training funding and QR funding.

22. Annex B contains the criteria for inclusion of data in the allocations of per capita, premium, PGR training funding, race equality funding, well-being and health funding and targeted employability support funding.

23. Annex C contains the eligibility criteria for data used in the calculation of the National Measures.

24. Annex D contains documentation supplied to HEPs to support Fee and Access Plan sign off.

25. Annex E contains a summary of recommendations from previous internal audits.

Teaching funding

26. 2024/25 teaching funding comprises:

• Funding allocated through the credit based teaching funding method for part-time undergraduate taught provision;
• Per capita funding for full-time and part-time taught provision;
• Expensive subjects premium funding for full-time undergraduate provision;
• Higher cost subjects premium for full-time undergraduate provision;
• Access and retention premium funding for part-time undergraduate provision;
• Disability premium for all modes and levels of study;
• Welsh medium premium for part-time undergraduate provision and full-time undergraduate provision that qualifies for expensive subjects premium or higher cost subjects premium funding.

27. Funding allocated for part-time undergraduate provision through the credit based teaching funding method for 2024/25 was based on 2022/23 End of Year Monitoring of Higher Education Enrolments (EYM) credit value data extracted through the HESA Information Reporting Interface Service (IRIS) process. HEFCW circular W23/26HE details the 2022/23 EYM extraction process and mappings.

28. Adjustments to credit based teaching funding are normally calculated using EYM data extracted during the HESA IRIS process. The 2022/23 adjustment process has taken place and the data extracted is described in the 2022/23 EYM circular W23/26HE. The latest data extraction is described in the 2023/24 EYM publication Medr/2024/00 though the adjustments for 2023/24 have not yet been calculated.

29. Testing of the systems and processes used to generate figures returned on the Higher Education Students Early Statistics (HESES) survey and EYM data returned on the HESA student record and extracted via HESA IRIS should aim to answer the following questions:

• Is the latest HEFCW/Medr guidance being utilised and adhered to, in particular, have changes from the previous HESES surveys been noted and appropriately implemented?
• Are data on the records system validated (e.g. a comparison of a sample of enrolment forms with data on the system)?
• Is the method of extraction of data used to make a return to the HESES survey documented?
• Is there an adequate audit trail to confirm that the method of data extraction for the surveys is being applied as documented?
• Are details of any manual amendments to data extracted from the system for the HESES survey, or to EYM data extracted via HESA IRIS, documented, with justification and/or appropriate authorisation of the changes?
• Is a copy kept of the data taken from the system to make the return to the HESES survey?
• Is the final return to the HESES survey checked against data on the system prior to submission and is there adequate evidence of this checking process?
• Is the EYM data extraction provided through the HESA IRIS system checked against data on the HEP’s internal system and is there evidence of this checking process prior to the data verifications being signed off?
• Is the verification approved and signed off by an appropriate person?
• Are the staff resources available, taking into consideration experience and expertise, adequate to ensure that the HESES survey returns are accurately prepared and the EYM extraction from the HESA IRIS system is thoroughly checked?
• Is the documentation of the system and staff resource sufficient to ensure that accurate data returns could be prepared even in the absence of some key staff?
• Is there a risk register in place and are the risks relating to the compilation of accurate data returns, and related controls to manage these risks, adequately assessed and documented together with details of planned action to be taken, where relevant, to strengthen the existing controls?
• Where errors were identified in HESES/EYM returns or sign-offs, by HEFCW/Medr or the HEP, have processes been implemented to address these data errors and to mitigate against errors in future returns and sign-offs?
• Are HESES survey returns scrutinised before submission by suitably experienced members of staff other than those compiling the return?
• Are EYM data extracted as part of the HESA IRIS system scrutinised before verification by suitably experienced members of staff other than those that compiled the HESA return?
• Is a summary report of the data returned presented to the HEP’s senior management team (e.g. the total numbers of credits and students by mode and level with comparisons to prior years and/or other returns)?
• Is there a suitable process in place to ensure that staff who provide information (e.g. in departments) and staff compiling the return liaise as necessary to ensure that the most up to date information available relating to the survey period is included in the return?
• Is there evidence that validation and credibility checks are completed before returning or signing off data (e.g. scrutinising the credibility checks provided by HEFCW/Medr on the Excel spreadsheets; comparing EYM/HESES data against HESES returns made earlier in the academic year or in the previous academic year; use of control totals)?
• Has the Explanations worksheet in the EYM workbook been completed where year on year differences require explanations?
• Are there procedures for determining the fundability status of students and are checks made on fundability status (e.g. for students located outside Wales); and have the fundability rules contained in HESES been accounted for in the determination?
• Is the method for assigning Higher Education Classification of Subjects (HECoS) codes to modules and hence categorising credits into Academic Subject Categories (ASCs) documented and reasonable (for any data relating to 2019/20 onwards)?
• Is there an adequate audit trail to confirm that the method for categorising credits into ASCs is being applied as documented?
• Are processes used by HEPs to calculate estimates (e.g. non-completion rates) reasonable and documented, and is their reliability tested?
• Do processes ensure that evidence of enrolment and attendance available is complete and retained as part of the audit trail (e.g. enrolment forms, online enrolment records, module choice forms)?
• Are franchised out students correctly identified as such on the system, and recorded as such on the returns, and not, for example, as distance learning students (where distance learning students are those that are students of the reporting HEP, where staff employed by the reporting HEP are responsible for providing all teaching or supervision, but who are located away from the reporting HEP and are not part of a franchising arrangement with another HEP or organisation)?
• Are arrangements with franchise partners documented and are there controls in place to ensure that only the franchisor returns the provision?
• From 2024/25 HESES onwards, are degree apprentices on the Medr funded degree apprenticeship scheme recorded correctly both for enrolments and associated assumed completed credit values.
• If the HEP has recently been formed from a merger are the data systems in place sufficiently integrated to enable the HEP to make returns for the whole HEP and manage the process of validating and verifying data?

30. For 2024/25 funding, per capita and premium funding is based on data taken from the 2022/23 HESA student record (coding manuals and guidance are available on the HESA website – www.hesa.ac.uk). In looking at the above questions, in any in analysis of student data, it is not expected that auditors will look in depth at systems and processes related to 2022/23 HESA student record data, as described in paragraphs 6 and 14, but that any in depth testing carried out would be on the systems and processes for 2022/23 data used for 2024/25 funding.

31. HESES data is not used in allocation of 2024/25 teaching funding, however it is required to monitor student recruitment and to provide to the Welsh Government for student and, up to 2023/24 HESES, Initial Teacher Education (ITE) planning. Additionally, from 2024/25 onwards, HESES data is used in allocation of in-year funding for degree apprentices on the Medr funded degree apprenticeship scheme. Testing will be similar to that of the systems and processes of the EYM extractions and as described in paragraph 29.

Data Requirements

32. The fields and criteria used to extract data from the records for 2024/25 funding and monitoring of funding are detailed in the HEFCW Higher Education Data Requirements circular W23/27HE The HESA student record data used in 2024/25 funding and monitoring of funding in the main is 2022/23 data which was the first record collected since the implementation of HESA Data Futures.

33. In looking at the scope of the audit, in any in analysis of student data and the associated systems and processes, including the suggested testing below, it is expected that auditors will look at 2023/24 HESA student record data submission, using guidance included in paragraphs 6 and 14.

34. Testing of the systems and processes used to make these returns should aim to answer the following questions:

HESA student record:

• Do the controls include quality checks on individualised data prior to submission to HESA, in particular for data fields used in funding (e.g. checks that home postcodes have been correctly transcribed; HECoS codes are correctly assigned; fundability status is correct; year of student is correct; those in receipt of disabled students’ allowance (DSA) are recorded as such)?
• Where errors were identified in prior returns, by HEFCW/Medr, HESA or the HEP, through audit, in Medr/HEFCW data quality meetings or otherwise, particularly those which led to reductions in funding, have processes been implemented to address these data errors and to mitigate against errors in future returns?
• Have any issues that have been raised via the HESA Issue Management System (IMS) and any associated targets applied been collated and considered to make improvements in future data submissions?
• Where errors have been identified in prior returns, are the relevant data checked prior to final submission of data to HESA to confirm that the error has not reoccurred?
• Is there evidence that the web reports and IRIS output, produced by the HESA data returns system after committing data, are scrutinised, and that any resulting issues are addressed?
• Has a review of the implementation of HESA Data Futures been carried out and any updates to systems or processes been actioned along with any associated changes to risk registers?
• Is a copy kept of the final data submitted to HESA?
• Is the method used to calculate the proportion of a module taught through the medium of Welsh documented, reasonable and consistently applied?
• Are any manual amendments made by HEFCW/Medr to exclude Welsh medium modules checked to confirm they have been correctly excluded?
• Are any changes made to include additional information requested, or manual amendments made to the Degree Apprenticeship monitoring extracts, checked to confirm they are accurate and adjusted totals are correct?
• Are any manual amendments made by the provider to the monitoring returns output from IRIS for the part-time fee waiver and PGT Master’s bursaries documented and scrutinised before sign-off?
• Are the staff resources available, taking into consideration experience and expertise, adequate to ensure that the data returns are accurately prepared?
• Is the documentation of the system and processes and the staff resource sufficient to ensure that accurate data returns could be prepared even in the absence of some key staff?
• Is there a risk register in place and are the risks relating to the compilation of accurate data returns, and related controls to manage these risks, adequately assessed and documented together with details of planned action to be taken, where relevant, to strengthen the existing controls?
• Are returns scrutinised before submission by suitably experienced members of staff other than those compiling the return?
• Is a summary report of the data submitted to HESA presented to the HEP’s senior management team (e.g. numbers of students by mode and level and/or course and subject with comparisons to prior years and/or other returns)?
• Are the HEFCW/Medr confirmation and verification reports checked against data submitted to HESA to ensure that the HEFCW/Medr reports are accurate according to HEFCW/Medr criteria?
• Where, in addition to their directly funded provision, the FEI franchises provision in, are there controls in place to ensure that only the franchisor returns the provision to HESA?
• If the HEP has recently been formed from a merger are the data systems in place sufficiently integrated to enable the HEP to make a HESA student record return for the whole HEP?

National Measures

35. The systems and processes used to return data used in the monitoring of National Measures for 2017/18 and onwards, for HEIs, are within the scope of the audit for the following set of measures:

• Widening access;
• Participation;
• Retention;
• Part-time;
• Welsh medium;
• Student mobility;
• Continuing Professional Development;
• Total HE-BCI income per full-time equivalent (FTE) of academic staff;
• Spin off activity;
• Start – up activity (graduate);
• Research Staff;
• PGR students;
• PhDs awarded;Research income;
• EU/Overseas students;
• EU/Overseas staff;
• Transnational Education.

36. A subset of the National Measures are included in the scope of the audit for FEIs:

• Widening Access;
• Participation;
• Retention;
• Part-time;
• Welsh medium.

37. HESA UK performance indicator (PI) data, which are derived from HESA student record data, were used in the calculation of the participation and retention National Measures. HESA previously produced PIs on behalf of all the HE funding and regulatory bodies of the UK and announced that 2022 would be the last year that PIs would be published and indicators will be reviewed for migration into Official statistics or Open data. However at the present time there are no updates to the UK PIs used to monitor participation and retention. This means that 2020/21 academic year data were the last used to produce PIs in their current form. More information about the UK performance indicators can be found on the HESA website. While we are unable to update the retention measure for 2021/22 and 2022/23, we have been able to update the participation measure for both 2021/22 and 2022/23. HESA kindly provided us with the 2021/22 data calculated using the UKPI methodology as a one-off, and we have calculated 2022/23 using a methodology which follows HESA’s participation methodology as closely as possible.

38. The fields and criteria used to extract the data used in monitoring these measures are detailed in the Higher Education Data Requirements circular (HEFCW circular W23/27HE). Testing of systems and processes used to return data that are used in funding will cover most of the testing appropriate for HESA data used in monitoring National Measures. In any testing of the HESA student record, auditors should take note of the guidance in previous paragraphs relating to the 2023/24 HESA student record, particularly in paragraphs 6 and 14. In addition to the points in paragraph 34, testing should aim to answer the following questions:

HESA student record:

• Do the controls include quality checks on individualised data prior to submission to HESA, in particular for data fields used in monitoring (e.g. checks that the student’s mobility experience data is correct)?
• Is there evidence that for National Measures data extracts contained in the IRIS output produced by the HESA data returns system after committing data, is scrutinised, and that any resulting issues are addressed?

HESA Higher Education Business and Community Interaction (HEBCI) survey:

• Are HEBCI survey definitions and guidelines utilised and adhered to?
• Are validation and credibility checks carried out before returning data (e.g. comparisons with previous year’s data)?
• Are the methods and processes used to collate and extract data documented?
• Is there an adequate audit trail to confirm that data extraction methods are being applied as documented?
• Are the staff resources available, taking into consideration experience and expertise, adequate to ensure that the data returns are accurately prepared?
• Is the documentation of the systems and processes and the staff resource sufficient to ensure that data returns could be prepared even in the absence of some key staff?
• Is there a risk register in place and are the risks relating to the compilation of data returns, and related controls to manage these risks, adequately assessed and documented together with details of planned action to be taken, where relevant, to strengthen the existing controls?
• Are returns scrutinised before submission by suitably experienced members of staff other than those compiling the return?
• Is a summary report of the data returned presented to the HEP’s senior management team (e.g. the items of data used in Corporate Strategy targets with comparisons to prior years and/or other returns)?
• Is there a suitable process in place to ensure that staff who provide information (e.g. in departments) and staff compiling the return liaise as necessary to ensure that the most up to date information available relating to the survey period is included in the return?
• Are processes used to calculate estimates reasonable and documented, and is their reliability tested?
• If the HEP has recently been formed from a merger are the systems in place sufficiently integrated to enable the HEP to make a HEBCI survey return for the whole HEP?
• Do the controls include a reconciliation of the total amount of income recorded on the HE-BCI survey from collaborative research, consultancy, contract research, continuing professional development, facilities and equipment related services, intellectual property and regeneration and development returned with the audited accounts to ensure consistency?

HESA finance record:

• Are definitions and guidelines utilised and adhered to?
• Are validation and credibility checks carried out before returning data (e.g. comparisons with previous year’s data)?
• Are the methods and processes used to collate and extract data documented?
• Is there an adequate audit trail to confirm that data extraction methods are being applied as documented?
• Is a copy kept of the final data submitted?
• Are the staff resources available, taking into consideration experience and expertise, adequate to ensure that the data returns are accurately prepared?
• Is the documentation of the systems and processes and the staff resource sufficient to ensure that data returns could be prepared even in the absence of some key staff?
• Is there a risk register in place and are the risks relating to the compilation of data returns, and related controls to manage these risks, adequately assessed and documented together with details of planned action to be taken, where relevant, to strengthen the existing controls?
• Are returns scrutinised before submission by suitably experienced members of staff other than those compiling the return?
• Is a summary report of the data returned presented to the HEP’s senior management team (e.g. the items of data used in Corporate Strategy targets with comparisons to prior years and/or other returns)?
• Is there a suitable process in place to ensure that staff who provide information (e.g. in departments) and staff compiling the return liaise as necessary to ensure that the most up to date information available relating to the survey period is included in the return?
• Do controls include a reconciliation of the returned Research income values with the audited accounts to ensure consistency?

HESA Staff record

• Are quality checks carried out on individualised data for data fields used in National Measures (e.g. nationality, academic employment function)?
• Where errors were identified in prior returns, by Medr/HEFCW, HESA or the HEP, through audit or otherwise, have processes been implemented to address these data errors?
• Where errors have previously been identified in data used in National Measures, are the data checked prior to final submission of data to HESA to confirm that the error has not reoccurred?

HESA Aggregate Offshore Record

• Are quality checks carried out on headcount data used in the Transnational Education National Measure?

PGR and QR Funding

39. More information about the funding methodology for both the PGR training funding allocation and the QR funding allocation, which were revised in 2022/23, can be found in circular W22/24HE.

40. PGR training funding for 2024/25 was allocated using data about eligible, fundable student FTEs in REF 2021 units of assessment (UoAs) which qualified for QR funding taken from the 2022/23 HESA student record. Students eligible to be included in the calculation of PGR funding are those in REF 2021 units of assessment (UoAs) that were included in the QR funding model for 2022/23.

41. The fields and criteria used to extract the data from the record for 2023/24 funding are detailed in the Higher Education Data Requirements circular Medr/2024/01. In any testing of the HESA student record, auditors should take note of the guidance in previous paragraphs relating to the 2023/24 HESA student record, particularly in paragraphs 6 and 14. In addition to the points in paragraph 29, testing should aim to answer the following questions:

HESA student record:

• Are quality checks carried out on individualised data for data fields used in calculating PGR funding (e.g. fundability status is correct; UoA is correct; student FTE is correct; postcode and domicile are correct)?
• Are the Medr confirmation reports checked against data submitted to HESA to ensure the Medr reports are accurate according to Medr criteria?
• Where errors were identified in prior returns, by Medr, HESA or the HEP, through audit or otherwise, particularly those which led to reductions in PGR funding, have processes been implemented to address these data errors and to mitigate against errors in future returns?
• Where errors have previously been identified in PGR data, are the PGR data checked prior to final submission of data to HESA to confirm that the error has not reoccurred?

42. Following the implementation of the new funding methodology for QR funding allocations for 2022/23, all input data were frozen. Therefore data used to calculate 2024/25 QR funding remain the same as those used to calculate 2022/23 QR funding. Data used to calculate 2022/23 QR funding were taken from REF 2021, and from the 2018/19, 2019/20 and 2020/21 HESA finance record . The REF 2021 is not included in the scope of the audit.

43. Checks on the systems and processes used to return data relating to the student finance data from the particular years used in the QR funding allocation are included in the scope, only where they have not been included in previous audits and this is considered to be an area of risk. The questions these checks should aim to answer are outlined in the section above.

Research Wales Innovation Fund (RWIF)

44. This funding stream is calculated using data from the HE providers HESA HEBCI survey and from their HESA staff, student and finance records.

45. The details of this process can be found in HEFCW circular W23/12HE and the allocations for 2024/25 are outlined in HEFCW circular W24/13HE. Testing should aim to answer the following questions (in addition to those listed for other funding streams above):

HESA student record (Open University in Wales only):

• Do the controls include quality checks on data prior to submission, in particular for the data fields used for RWIF (e.g. that student FTE is returned correctly)?

HESA Higher Education Business and Community Interaction (HEBCI) survey:

• See the HEBCI questions in paragraph 38.
• Do the HEBCI values signed off during the RWIF verification frequently differ from those values submitted to HESA?

HESA finance record:

• See the HESA finance record questions in paragraph 38.

HESA Staff record

• Are quality checks carried out on data for data fields used in this return (e.g. that academic Staff FTE is returned correctly)?

Data returned on fee and access plans and fee and access plan monitoring returns

46. Fee and Access Plans covering two years were submitted in 2024. The approved plans covered the 2025/26 and 2026/27 academic years.

47. Fee and Access Plans were returned in line with guidance included in HEFCW circular W24/07HE Fee and Access Plan guidance. Data required for HEI submissions were limited to total numbers of students forecasted for study at each of the institutions’ location of study. Detailed guidance for this can be found in paragraphs 157 to 165 in HEFCW circular W22/19HE. In addition to this, FEIs were required to submit information on total fee income to be received and financial information. Guidance for this can be found in W22/19HE in paragraphs 155-156 and 166-173 respectively.

48. Institutions were invited to provide applications for Fee and Access Plan variations in March 2024 further to an increase in tuition fee limits made by Welsh Government in February. As part of that process, institutions were required to submit a tracked change version of their original Plan, alongside a variation request form. In submitting the variation, governing bodies of those institutions were confirming that they:
i) were compliant with CMA requirements and have taken appropriate legal advice;
ii) had consulted students on the variation;
iii) involved student representatives in the approval process;
iv) would continue to invest their agreed proportion of tuition fee income with no reduction to the proportion of investment to promote equality of opportunity; and
v) had involved partner providers where fee levels are being varied at courses delivered under franchise arrangements.

49. Fee and Access Plan monitoring is incorporated into the annual assurance return process. Institutions’ governing bodies are required to sign off the following statements in relation to Fee and Access Plans:

• No regulated course fees have exceeded the applicable fee limits, as set out in the 2023/24 Fee and Access Plans.
• The institution has assurances in relation to the management of the provision of fee information across all recognised sources of the institution’s marketing.
• The institution has taken all reasonable steps to comply with the general requirements of the 2023/24 Fee and Access Plans.
• The institution to provide documentation to support Fee and Access Plan sign off.
• The institution has taken all reasonable steps to maintain previous levels of investment, including maintaining:
  • the splits between investment to support equality of opportunity and promoting higher education,
  • investment to support the Reaching Wider partnership and student support investment.

50. The documentation produced internally that enables the governing body to sign off its annual assurance statement must be submitted alongside the annual assurance return. These documents enable us to understand the basis on which the governing body was able to sign off the Fee and Access Plan related statements of the annual assurance return. In addition to this, we also require documentation to be submitted to evidence how institutions evaluate the effectiveness of investment to deliver on Fee and Access Plan objectives. Auditors should familiarise themselves with the data required to enable the governing body to sign off this part of the statement and to inform the evaluation of the effectiveness of the Fee and Access Plan. Guidance to inform institutions is provided at Annex D.

Other HESA data

51. Other HESA data not covered in the previous paragraphs that are also under the scope of the audit include data returned on the HESA finance record, aggregate offshore record, Estates Management record, HEBCI survey and data returned on the HESA Unistats record.

52. Testing of systems and processes used to return data that are used in National Measures and RWIF funding (see relevant sections above) will cover most of the testing appropriate for HESA HEBCI survey data and HESA finance record data.

53. The Unistats dataset contains information about courses. Included in the scope of an audit of Unistats data are course related data and accommodation cost data. Testing should aim to answer the following questions:

• Have eligible courses been returned on the Unistats dataset and are the data for those courses accurate?
• Where data have been estimated, have estimates been made on a reasonable basis and documented?

54. The following funding streams were also allocated:

• Higher Education Research Capital (HERC) Funding 2024/25 (W24/14HE)
• Capital Funding 2024-25 (W24/12HE)

The audit of systems and processes used in other funding streams is sufficient to also provide assurance for the funding streams listed in this paragraph.

HESA Data Futures Programme

55. Data Futures is Jisc’s transformation programme for collecting student data, and was implemented for the 2022/23 HESA student record collection.

56. The 2022/23 and 2023/24 collections were an annual collection using the Data Futures data model. The 2024/25 collection will continue to be an annual collection.

57. Auditors should familiarise themselves with the programme and the requirements for the new record from 2022/23 and into 2023/24. We recommend that any review of the 2023/24 HESA student record should follow the guidance as described in paragraph 6, given the continuing difficulties that providers encountered in returning the record. We would expect auditors to provide opinions on the controls in place to manage risks relating to the record going forward including plans to review and/or improve processes, documentation and data quality using lessons learnt from the return of both 2022/23 and 2023/24 data, moving into the 2024/25 return, even if those processes or plans are not yet in place.

58. Testing should aim to answer the following questions:

• Did the HEP have sufficient resource, in terms of both finance and suitably skilled staff in making the 2023/24 return?
• Were senior management aware of any issues that their provider encountered for the 2023/24 return?
• Is there a plan in place to review any data quality issues, targets set resulting from IMS queries, or to put in place any lessons learnt from the 2022/23 and 2023/24 returns, to improve future returns?

Interpretation and Guidance

59. Auditors should familiarise themselves with the latest, at the time of audit, HESES, EYM, HESA guidance (including for the HEBCI survey and finance record), data requirements circular and where available, the fee and access plan process and guidance. Some of the publications may be updated after publication of this publication and auditors should pay particular attention to any changes made to the data collected that imply changes to the way in which systems and processes work and assess whether HEPs have made or intend to make appropriate adjustments.

60. Any further clarification relating to the guidance for making HESES, EYM, HESA returns or extracting EYM data from the HESA student record via the IRIS system or relating to fee and access plan guidance can be obtained from Medr via [email protected].

Open University in Wales

61. Medr has responsibility for some funding relating to teaching and RWIF at the Open University (OU) in Wales. Teaching and RWIF funding allocated to the OU in Wales is calculated using the same funding methodology as other HEIs. As in previous years the systems and processes used to compile data returns to HESA and Medr that are used in the calculation of teaching and RWIF funding are included in the scope of the internal audit. In addition, the OU in Wales is included in the National Measures and so the systems and processes used for monitoring these are included in the scope of the audit. The OU in Wales does not currently receive PGR or QR funding from Medr and as the OU are not a Medr regulated institution, do not submit a fee and access plan.

Reporting

62. The annual internal audit plan should include a review of the controls in place to manage the risks relating to the submission of accurate data returns and where appropriate, data returned in and used to monitor the FAPs.

63. This review should include an assessment of the adequacy of the controls documented in paragraphs 29 to 58 above as relevant. However, the precise scope of the internal audit work completed will be determined by each HEP’s assessment of the risks relating to their HEP’s data return and it is expected that the internal audit work will focus on the higher risk aspects of the systems and processes, for example, issues identified in previous audits, or aspects not covered in previous audits. It is expected that the scope would address any data issues or errors found by the HEP or HEFCW/Medr in terms of processes in place to correct the errors and to mitigate against any future errors. In assessing the risks, we would expect the HESA student record return for 2023/24 to be an area of risk, however, providers should take account of the guidance provided in paragraphs 6 and 14 in relation to the 2023/24 record when determining the scope of the audit work.

64. The timing of the internal audit work should be arranged so that the internal audit report can be completed and presented to the HEP’s Audit Committee before a copy of the report is sent by the HEP to Medr by 27 June 2025.

65. Where the Audit Committee’s internal audit plan includes only very limited work in relation to data systems and processes, because there is perceived to be low risk in this area, an institutional representative should contact Medr to inform us why this area is considered low risk and how annual assurance can be obtained in these circumstances. The representative should contact Medr at the point that their Audit Committee finalises their audit plan if this is the case. Similarly, if there are any changes to the cyclical nature of the plan or timing of committees that mean that an audit report will not be available by the deadline of 27 June 2025, a representative should contact Medr to discuss.

66. The internal audit report should include:

• A description of the objectives of the audit and the risks and controls included within the scope of the audit;
• Details of the audit work completed;
• Details of issues identified during the audit and the recommendations made to address these;
• Details of processes put in place to correct the errors and to mitigate against any future errors of any data issues or errors found by the HEP or HEFCW/Medr;
• A consideration of the recommendations made in previous audit reports and the extent to which these have been effectively implemented;
• Management’s responses to the report’s recommendations and the agreed timescales for their implementation;
• Details of any disagreements or recommendations which were not accepted by management;
• A clear conclusion and overall opinion as to the adequacy and effectiveness of the controls in place to manage the risks relating to the accuracy of the data returns included within the scope of the audit.

67. If the internal audit report’s overall conclusion, or the conclusions relating to the adequacy of the design of the system of control and the application of those controls, provides a negative opinion (e.g. limited or no assurance, unsatisfactory or inadequate controls) details of the significant exceptions giving rise to this opinion should be provided in the report. In these circumstances the HEP’s Audit Committee and Medr should be informed of the relevant issues as soon as possible.

68. The HEP’s Audit Committee should include reference in its annual report to the reports and assurances that it has received during the year in respect of the controls in place to manage the quality of data returns made by the HEP for funding or monitoring purposes and the controls relating to data returned in and used to monitor the fee and access plans.

69. An electronic copy of the audit report and any associated correspondence should be sent by the HEP to [email protected] no later than 27 June 2025. Note that we do not require a paper copy to be sent to us.

70. Details of the internal audit work and reports completed since the last external audit of higher education data should be retained and if required be made available to any external auditors as advised by Medr. The Medr Audit Service may also wish to review these reports and related papers during their periodic visits to the HEP.

Further information

71. Further guidance and information is available from Rachael Clifford or Hannah Falvey ([email protected]).

Medr has today welcomed its first advice from the Coleg Cymraeg Cenedlaethol on its duties relating to the Welsh language, reflecting their shared ambitions to encourage the demand for, and the participation in, tertiary education provided through the medium of Welsh.  

The advice, published by the Coleg, follows the Coleg’s designation to advise Medr, on its duty to promote tertiary education through the medium of Welsh, and has been considered in detail by our Board.  

The Coleg’s advice sets out steps that Medr and the tertiary education sector should take in supporting the Welsh Government’s ambitions for the Welsh language and the goals of Cymraeg 2050. This includes a central recommendation that Medr should develop a National Plan for the Welsh language across the tertiary education sector, which the Medr Board has agreed.

Medr’s Strategic Plan will be submitted to Welsh ministers in December 2024. The advice from the Coleg, combined with engagement across the tertiary education sector, has played a crucial role in shaping how Medr will respond to its strategic duties to the Welsh language.

Simon Pirotte OBE, Chief Executive Officer of Medr, said: “The relationship with the Coleg Cymraeg Cenedlaethol is of vital importance to Medr. We are united in our desire to see the Welsh language thriving in Wales, and committed to ensuring the tertiary education sector plays its part in realising a vision for a million speakers in Wales by 2050. The advice we have received marks the start of a significant new phase of working together, building on the positive foundations we have already established. A National Plan will be a crucial driver in enabling more learners to develop, maintain and use their Welsh language skills.”

Medr will continue to fully take account of the Coleg’s advice as it implements its Strategic Plan 2025-2030 in partnership with all stakeholders.