Attendance in Sheffield Schools

1 Introduction

1.1 Background & scope

This work was undertaken by the Sheffield City Council Business Intelligence team from around September 2023. New analysis was carried out on available data with the aim of understanding school attendance Sheffield and informing the requirements of the city’s response. This report summarises the findings of that analysis, along with commentary derived from discussions of those findings with colleagues in SCC, Learn Sheffield and from Sheffield schools.

This report covers the following:

• recent trends
  
• benchmarking and comparisons
  
• key drivers of absences
  
• demographic differences:
  
• age
  
• gender
  
• ethnicity
  
• geography & deprivation
  
• distance to school
• young carers
• severe absence (<50% attendance)
• the absence patterns of annual year cohorts
• day level data analysis - mapping out a school year

Within the same analysis but out of scope of this report are:

• special educational needs (this is covered in depth in the SNA report)
• the performance of individual schools
  
• exclusions
  
• the reach and effectiveness of existing teams, services and interventions

some terms & definitions

Unless otherwise stated, absence refers to both authorised and unauthorised absences. Correspondingly, attendance refers to registered time in the classroom. Absence in this report may include periods of study leave, approved offsite activity

Unless otherwise stated, the word year refers to the academic or exam year. So 2023 refers to the period of schooling between September ’22 and July ’23.

1.2 Data sources & processing

Attendance, exclusion and school registration data and student details used in this report are from Capita One, retrieved from the OSCAR database, which is maintained by the Performance & Analysis Service (PAS). Supplementary information on school types and locations, geography & deprivation are held in spreadsheets.

An R script gathers, combines, processes and aggregates this data into a data model. That data model was last updated 16/8/24 to include the first release of the full year 2024 attendance data.

1.3 Release notes

1/7/24 - Giles Robinson. First complete draft for circulation.
16/8/24 - GR - updated with latest data, full 2024 academic year, various revisions, analysis of daily data; young carers
9/5/24 - GR - significant update with data now available up to Easter 2025.

2 Trends

Recent changes in overall attendance, by the major reasons covered by Department for Education (DfE) absence codes. We also discuss some codes that do not count as absences, but contribute to the picture around attendance, such as late present.

2.1 Overall attendance

The COVID-19 pandemic and lockdowns saw a significant drop in attendance rates - although many of these trends beggan before the pandemic. Secondary age pupils were affected more than those in primary. In 2024 a gap continued to grow, with primary school attendance improving but worsening in secondary (though this was, in part, a result of the return of study leave as a coded reason, accounting for around 1% of secondary absences).

Important

At the time of writing (May 2025), there has been a significant shift into the current year. Primary school attendance continues to improve, while secondary has improved sharply on the previous year.

Our data prior to 2018 is less reliable and less complete, but taking a longer view suggests that at least some of the drivers of recent trends predate the pandemic. Attendance was improving to a peak in 2016, and was gradually dropping away from there, particularly in secondary schools. Things were getting worse before COVID, but the pandemic changed everything - despite recent improvements, attendance in Secondary schools remains well below pre-pandemic levels.

2.2 Illness

The recorded data on illness shows an increase year on year. The big rise into 2022 particularly affected primary age children, and was probably a mixture of COVID-19 itself and post-lockdown viral bounce-back. Illness rates increased slightly into 2025.

Caution

Patterns in the day level data, and feedback from head teachers suggests that we are probably not seeing the true picture on illness. Differences in reporting (and the honesty of parents), policy and recording may be as significant here as changes in actual illness.

recomendation

The data on illness is worth monitoring in 2025 and considering in relation to schools’ recording policies, particularly at the day level, and in proximity to bank holidays and half term holidays. It’s possible that the new DfE rules around penalty notices and family holidays will create perverse incentives to increase reported illness rates, especially as we head into the 2025 summer term. It might be detectable in the day level data (see later in this report).

2.3 Lateness

Head teachers report that lateness, even if marked as late present can have a significant can impact on activies that are regularly done first thing in the day, such as phonics. Lateness can be recorded as late present or late absent, the latter meaning that the child attends only after the registers have closed. Both categories are on the rise, with late absence in primary schools in particular growing problem. In secondary schools, late present is more common - and has been rising in recent years.

Late absent (after registers closed) make up a much smaller percentage of available sessions, but in secondary schools this is dramatically up in 2025 (note that this data does not yet include the summer term):

2.4 Family holidays

Absences for family holidays are higher in primary, but have risen in secondary also. The cost of living crisis likely plays a part here. Rates appear lower in 2024, but at the time of writing the summer term is missing from the 2024 data, which is excluded from the plot below. Family holidays can be authorised or unauthorised, but due to differences in recording and coding policy between schools, both are grouped together here.

New DfE guidance and harsher penalties around family holiday absences came into effect August 2024, and the chart above appears to show a significant reduction, though this is mostly due to the summer term data is not yet (as of May ’25) being available. Breaking this down by term shows two things:
- in all previous years (COVID period aside, and the lockdown years have been removed here), the summer term has been the time when most holidays are taken - so far in 2025, the impact of the new rules is apparent in the autumn and spring term data - though the impact appears small, and levels are still well above pre-COVID years. The real test of this policy will be the rates in the summer term

2.5 Exclusions

Exclusions have risen very rapidly, particularly in secondary schools. This is mostly driven by temporary suspensions, largely as schools clamp down on what is classified as persistent disruptive behaviour. This makes only a small contribution to overall absence rates, but is growing, and for some children is a major contribution to their overall school absence. Exclusion rates in 2025 (part year at the time of writing) look to have levelled off.

2.6 Study leave

2024 saw seen the return of study leave as a coded absence reason, with a significant impact on overall attendance levels in secondary and particularly y11. At the time of writing the 2025 data does not include the summer term and shows only minimal study leave.

2.7 No reason

The plot below comprises two DfE codes. Code N is intended as a placeholder until schools can establish a reason for absence, and code 0 is for unknown or other circumstances. Here both are grouped together - though the bulk is code O.

Important

The increase in no reason absences levelled off into 2024 and is down dramatically 2025. Some of this is likely due to the changes to recording from September 2024. Even so, the no reason category remains the biggest contributor to overall absence rates in secondary, and the increase in no reason absences are the biggest contribution to the post-pandemic rise in absences. Furthermore, no reason absences are significantly more prevalent in more deprived areas of the city, where attendance in general is poorer.

We can draw two possible conclusions from this: parents and children are not reporting the true reasons for absence, and the DfE codes are no longer suitable for capturing those reasons. In either case, this represents a serious blind spot in the data.

recomendation

Analysis of recorded case notes and text on Capita One, along with interviews or surveys of pupils, teachers, parents or community groups may help to understand the stories behind these no reason absences

Finally, the two charts below summarise the contributions of each of these coded absence reasons to the overall absence picture, during 2023-2024:

3 Demographics

Looking at how attendance varies with age, gender and ethnicity, and how this picture is changing over time.

3.1 Age

Absence is little higher in Y1 and Y2 when children are very young, and is level through primary. The transition to secondary school is associated with a big increase in absence, which continues year on year up to Y11. As we’ll see later on - this transition drop into Y7 and subsequent decline is more severe for groups with particular risk factors.

Note

The ImpactEd report Understanding Attendance - Report 1 identified an emerging trend of a jump in absence between Y7 and y8. The Sheffield data does not support this, with the increase from Y7 to Y8 looking broadly the same - around 1% increase in absence - as any other year on year increase within secondary years.

Looking at trends over time for primary school years, we see that the youngest and oldest primary age children were most affected. There are encouraging signs of recovery among all primary years into 2024, and particularly in Y1.

In secondary schools, we can see how disproportionately affected children in Y11, and encouraging signs of recovery in years 7 and 9. It is worth noting that the children in years 10 and 11 in 2024 were those who had their crucial Y6 and y7 transition years disrupted by the pandemic.

The drop off in Y11 is driven in part driven by study leave in 2024; this is yet to occur in the 2025 year

These trends will be explored in more detail in the Trends by annual cohort section later in this report.

3.2 Gender

Looking at overall school attendance since 2021, girls attend slightly better than boys, a difference of about 0.5%.

The gender time series show boys and girls moving in lockstep through primary school, separated by about half a percentage point:

In secondary we see boys’ attendance overtaking girls in the aftermath of the pandemic, but all continuing to decline into 2024.

Looking at age, gender and deprivation together, we see the pattern reversed in older children. In poorer wards of the city, girls consistently attend better than boys across all ages. In the most affluent wards, this is reversed in older children, with a gender gap widening from Y8 onwards, where boys have higher attendance.

3.3 Ethnicity

The ethnic makeup of Sheffield’s population continues to change, and there are differences in attendance rates between children in different ethnic groups. Here we summarise the data around ethnicity.

Caution

The ethnic groups and subgroups used in this analysis are those available the Capita One source data. These don’t necessarily align with the groupings used by ONS for census data, other organisations, or in other SCC data and reporting

With the caveat that data prior to 2018 may not be wholly complete, the attendance data allows us to look at a long term view of changes in the ethnic makeup of the Sheffield school population. Note the free y-axis scales on the following chart, means that the lines are not directly comparable:

Pupils and attendance in Sheffield by ethnicity description
pupils on roll in 2023/24; data from School Census & Capita One attendance records
	Total			Primary			Secondary
	count	% of pupils	% absent 2023/24	count	% of pupils	% absent 2023/24	count	% of pupils	% absent 2023/24
all children	73154	100.0%	8.5%	40342	55.1%	6.1%	32821	44.9%	11.7%
White British	41229	56.4%	8.5%	22372	54.3%	5.6%	18859	45.7%	12.0%
Black African and White/Black African	6223	8.5%	5.1%	3616	58.1%	4.1%	2607	41.9%	6.5%
Pakistani	5522	7.5%	8.2%	3133	56.7%	7.1%	2390	43.3%	9.7%
Any Other Ethnic Group	3144	4.3%	8.4%	1767	56.2%	6.9%	1379	43.8%	10.3%
Any Other White Background	2763	3.8%	9.3%	1546	56.0%	7.1%	1217	44.0%	12.1%
White/Black Caribbean	1971	2.7%	12.6%	1098	55.7%	8.7%	874	44.3%	17.9%
Other Asian Background	1863	2.5%	7.2%	1089	58.5%	6.2%	774	41.5%	8.7%
Gypsy, Roma and Traveller of Irish Heritage	1696	2.3%	21.2%	881	51.9%	16.0%	817	48.1%	27.0%
White/Asian	1679	2.3%	8.5%	958	57.0%	6.4%	722	43.0%	11.6%
Any Other Mixed	1623	2.2%	9.1%	934	57.5%	6.7%	689	42.5%	12.5%
not known	1443	2.0%	12.4%	607	42.1%	7.3%	836	57.9%	16.2%
Indian	1278	1.7%	6.1%	866	67.8%	5.8%	412	32.2%	6.7%
Bangladeshi	830	1.1%	8.1%	476	57.3%	7.1%	354	42.7%	9.6%
Any Other Black Background	773	1.1%	6.2%	450	58.2%	5.1%	323	41.8%	7.8%
Chinese	647	0.9%	4.1%	329	50.9%	3.5%	318	49.1%	4.7%
Black Caribbean	367	0.5%	9.1%	169	46.0%	5.8%	198	54.0%	12.1%
Irish	103	0.1%	8.3%	51	49.5%	4.8%	52	50.5%	12.0%

4 Geography & deprivation

There are many ways to divide up the city geographically, but we’ll look at the 28 wards, and in particular their deprivation as measured in the 2019 Indices of Multiple Deprivation (IMD) scores. More recent (and older) measures of deprivation may be available, but the analysis is broadly the same.

4.1 Attendance by ward

The table below shows overall attendance by ward of residence during 2023-24.

Pupils in Sheffield, by ward of residence
pupils on roll & attendance in 2023/24; data from School Census & Capita One attendance records
	Total			Primary			Secondary
	count	% of children	% absent 2023/24	count	% of children	% absent 2023/24	count	% of children	% absent 2023/24
Sheffield	73154	100.0%	8.5%	40342	55.1%	6.1%	32821	44.9%	11.7%
Burngreave	5720	7.6%	10.5%	3052	53.3%	8.1%	2672	46.7%	13.3%
Firth Park	4217	5.6%	9.5%	2369	56.2%	6.9%	1848	43.8%	13.0%
Darnall	4020	5.3%	10.2%	2368	58.9%	7.7%	1652	41.1%	14.2%
Manor Castle	3868	5.1%	9.5%	2188	56.6%	6.4%	1680	43.4%	13.8%
Shiregreen & Brightside	3656	4.8%	9.8%	1984	54.3%	6.7%	1673	45.7%	13.5%
Southey	3560	4.7%	10.7%	1975	55.5%	7.7%	1586	44.5%	14.4%
Ecclesall	3205	4.2%	4.7%	1727	53.9%	3.4%	1479	46.1%	6.3%
Gleadless Valley	3187	4.2%	9.7%	1787	56.1%	7.6%	1400	43.9%	12.6%
Nether Edge & Sharrow	2876	3.8%	6.6%	1603	55.7%	5.1%	1273	44.3%	8.5%
Park & Arbourthorne	2750	3.6%	9.9%	1555	56.5%	7.1%	1195	43.5%	14.4%
Beauchief & Greenhill	2702	3.6%	9.0%	1532	56.7%	6.8%	1171	43.3%	12.0%
Richmond	2553	3.4%	9.2%	1430	56.0%	6.5%	1124	44.0%	12.7%
Dore & Totley	2419	3.2%	5.3%	1340	55.4%	3.6%	1079	44.6%	7.3%
Woodhouse	2336	3.1%	9.9%	1320	56.5%	6.7%	1016	43.5%	14.0%
Hillsborough	2310	3.1%	7.8%	1275	55.2%	5.1%	1035	44.8%	11.6%
Stannington	2280	3.0%	6.8%	1234	54.1%	4.2%	1046	45.9%	10.0%
Walkley	2224	2.9%	7.3%	1366	61.4%	5.3%	858	38.6%	10.6%
West Ecclesfield	2168	2.9%	8.5%	1142	52.7%	5.5%	1026	47.3%	11.6%
Stocksbridge & Upper Don	2160	2.9%	8.8%	1134	52.5%	5.3%	1026	47.5%	12.7%
Birley	2041	2.7%	9.6%	1101	53.9%	6.3%	940	46.1%	13.6%
Graves Park	1988	2.6%	6.0%	1097	55.2%	4.3%	891	44.8%	8.1%
East Ecclesfield	1975	2.6%	7.5%	1032	52.3%	5.5%	943	47.7%	9.6%
Beighton	1913	2.5%	8.1%	1056	55.2%	6.2%	857	44.8%	10.7%
Crookes & Crosspool	1892	2.5%	6.1%	972	51.4%	3.9%	920	48.6%	8.4%
Fulwood	1733	2.3%	5.6%	933	53.8%	3.6%	800	46.2%	8.1%
Mosborough	1724	2.3%	8.1%	979	56.8%	6.1%	745	43.2%	11.1%
Broomhill & Sharrow Vale	1459	1.9%	6.1%	877	60.1%	4.9%	582	39.9%	8.1%
City	705	0.9%	9.2%	464	65.8%	8.2%	241	34.2%	11.4%

4.2 Economic deprivation

These ward level attendance figures line up neatly with deprivation indicators. Plotting attendance against the 2019 Indices of Multiple Deprivation (IMD) scores shows a tight correlation.

Caution

Since school attendance figures one of the input variables to the IMD scores, there is some circular logic at work here. Even so, attendance is only one of 39 inputs, so this analysis is worth pursuing.

The link to deprivation has always been there but is stronger today - recreating the chart above with 2010 attendance and IMD scores shows a weaker relationship.

The link to deprivation less evident in primary schools, but stronger in secondary schools, and the gap between primary and secondary attendance widens in poorer areas of the city.

This longer term view below compares the trend in attendance between the top and bottom quartiles of the ward level deprivation scores, at the half-term level with a trend-line. The middle two quartiles are excluded from this plot. The gap between the most and least deprived areas narrowed towards the peak attendance rate in 2016, so gains were disproportionately made in poorer areas, but the most deprived quartile then falls away more rapidly since the pandemic.

Finally here, since it’s not so easy to read from the above charts we we can look at the change in the difference in attendance between the most and least deprived quartiles of the city. Plotting this reveals that although attendance is increasing in both primary & secondary, and across all levels of deprivation, the gap between the most and least deprived quartiles of the city is reducing in primary schools, but continues to grow in secondary:

The age profile by deprivation quartile shows how children in poorer areas have a steeper drop off through secondary school. Children in the most affluent 25% of wards attend better across all years, but show a more significant dropoff into Y11. Could study leave be a factor here?

4.3 Free School Meals

Free School Meal (FSM) status is perhaps a better indicator of socio-economic status of children than ward of residence, since it is means tested at the family level.

Pupils in Sheffield, by free school meal status
count of pupils on roll in 2023/24; data from School Census & Capita One attendance records
	Primary			Secondary
	count	% of children	avg % absent (2023)	count	% of children	avg % absent (2023)
0	26477	65.6%	4.6%	21639	65.9%	8.4%
1	13865	34.4%	8.9%	11182	34.1%	17.9%
total	40342	55.1%	6.1%	32821	44.9%	11.7%

More concerning are the exclusion rates for children with Free School Meals, which are rapidly diverging from those without.

4.4 Distance to school

We used the postcodes of each child’s home address and school location to calculate a measure of straight-line distance between the two.

Attendance is significantly better, on average for children who live closer to school. Children living very close to school (<100m) attend about 1.5% better on average in Primary. For secondary schools this difference is 2.3%. Conversely,

Plotting the average distance travelled against average attendance rates for secondary schools reveals four groupings:

• on the right are two specialist facilities - UTC Sheffield & UTC Sheffield Olympic Legacy Park) and two catholic schools - All Saints and Notre Dame. All of these may incentivise pupils to travel further than normal.
• the main bunch of schools in the middle seems to show a linear relationship between distance and attendance. Though this relationship is weak, and relies on us discarding the outliers (more on these below), and may not be a causal relationship.
• Outlying this group above, Mercia, Tapton and High Storrs schools, are all in affluent areas of the city, and show higher attendance with average distance travelled
• Below this group Chaucer school shows average distance travelled and below average attendance. Though, as we’ll see below, the average distance travelled disguises some significant differences.

Plotting the distance travelled against attendance at the child level reveals further differences. In the plot below we take one example from each of the four groups described above.

We can think of dividing these plots into four quadrants:

Notre Dame High has good attendance across the board, which varies regardless of the distance travelled. Mercia has excellent attendance, and a limited distance travelled, presumably due to it’s oversubscription and high demand, with most datapoints appearing in the top left. The trend line points slightly down, as a few children who live further away have lower attendance.
Meadowhead has typical average values for both attendance and distance, appearing in the middle of the pack in the plot above. Most children attend well and those with poorer attendance generally live close by - there are few in the bottom right. Chaucer by contrast has a small but significant number of points in the bottom right quadrant - those who attend very poorly and live far away. Some of this may be explained by families failing to secure a place at closer schools, and being placed across the city, with the distance then contributing to poor attendance.

5 Young carers

It is difficult to establish the true number of young carers in the city - and perhaps dependent on definitions & methods. A 2023 all party parliamentary group (APPG) for young carers and adult carers report cites several sources:

• 1.6% of pupils (2021 Census)
• 0.5% of pupils (2023 school census) Though it places little confidence in these first two, preferring the estimates of two surveys:
• 10% of all pupils provide high or very high levels of care (BBC / University of Nottingham)
• 13% of pupils surveyed (COVID Social Mobility & Opportunities study)

Applying the 10% figure to Sheffield’s pupil population would indicate over 7000 young carers in the city. Our local data identifies just 904 since 2020, so we provide the analysis here with the following caveat:

data on young carers

The data used in this section of the report comes from young carer type involvements in capita one, covering around 900 children from 2020 onwards. Clearly our data doesn’t capture all young carers (and may skew towards those at the more severe end of the caring spectrum) and/or we are working with different definitions of what a young carer is. Issues with getting people of all ages to self-identify as carers are well known, and the perceived stigma attached to caring roles is likely more acute in young people - indeed this is probably a factor in explaining differences in school attendance.

The involvements have an open date, but no close date, so a time series analysis of volumes isn’t possible, and also that the data implicitly assumes that a young carer remains so for the rest of their school career.

A descriptive of demographic analysis may also be misleading, but we can make a comparison of attendance rates, which shows a significant impact. Primary age young carers attend just under 4% less that those without a caring role. In secondary school this gap rises to 10%:

As we did for deprivation quartiles above, we can create an age profile of attendance for young carers, and compare it to pupils with no caring role. Again we see the greater impact on attendance as age increases, and presumably the expectations and stigmatisation around caring roles also increases. There is a particular drop in attendance going into year 8.

Along with other groups, the attendance of young carers improved into 2025.

Note that some of the decline seen effect here may be a function of the cumulative nature of the data, which has no end dates attached, so our cohort of young carers is ageing in in the system

recomendation

Better long term data is required to understand volumes, impacts & the geographical distribution of young carers, as well as change over time and the provision of services to young carers.

6 Trends by annual cohort

He we show how attendance is changing for each annual year group cohort of children, and explore some of the intersectionality between age, deprivation and special educational needs. This analysis particularly demonstrates differences in how the COVID pandemic, lockdowns and subsequent societal shifts have affected different groups.

Annual cohorts of children are referred to here as, for example, the “class of 2025” meaning the year group who began year 1 in September 2014 and will complete Y11 in July 2025. In each case there is a separate small line chart for each annual cohort. Data are labelled with the academic year and the % attendance rates, and the time period is divided into three phases: pre pandemic, during (2020 & 2021), and post pandemic - all years since. The time periods are denoted by colours or shapes, depending on the chart.

The first chart shows the overall picture in secondary schools. The first cohort shown here is the class of 2020, who completed most of Y11 before the pandemic struck, their GCSE exams were wildly disrupted, but their attendance follows only a shallow decline from Y7 through to Y11, while the classes of ’23 to ’25 (on the middle row), saw dramatic drops during the COVID years, and a continued decline in the period since. The classes of ’24 and ’25 were perhaps worse hit by the pandemic, effectively missing Y6-7 and Y7-8 respectively. Finally, the bottom row shows the latest three cohorts and some small but encouraging signs of recovery: the class of ’27 have less of a drop off to Y8, and the class of ’28 had the best attendance in Y7 since before the pandemic.

The picture in primary schools looks very different. Children generally attend better in years 2 to 4 than they do in Y1, so the underlying profile is more of a hump than a steady decline seen in secondary. The pandemic had a less dramatic effect on primary age children, and the decline also persisted into the post-pandemic years for many cohorts. However the big difference here, and an encouraging sign for the future, is that all cohorts from the class of ’29 onwards show improvements in recent years (here coloured blue), and that the youngest cohorts are showing the fastest improvements of all.

Re-creating the same plot but split by deprivation quartile, it becomes clear how the effects of the pandemic were concentrated in the more deprived areas of the city. Here the middle two quartiles of deprivation have been removed, and the pairs of lines show the most and least deprived quartiles of the school population, according to the 2019 indices of multiple deprivation scores of their ward of residence.

For all annual cohorts, the gap is stark, children living in more deprived areas were worse affected during the pandemic and have seen worse post-pandemic declines in attendance. If there is good news here, it is a narrowing of the gap in the latest Y7 intake.

Repeating the same deprivation analysis for primary, and again we see how the pandemic disproportionately affected children in more deprived areas, with steeper dropoffs during the lockdown years. But we can also see recovery after the pandemic, for all cohorts and with steeper rates of increase for children in more deprived areas - but the deprivation gap still remains.

7 Severe absences

Children are classed as severely absent if they miss over 50% of available sessions in any given period. This section explores the characteristics of severely absent children, and how this is changing over time.

Important

Almost 1 in 20 children at Sheffield secondary schools was severely absent in 2023.

Severe absences in secondary schools appear to have peaked in 2024.

Next we look at the severe attendance rates of groups with different characteristics in 2023-24. The groupings here are chosen as those that show significant differences in severe absence rates. Note that the characteristics given here are not mutually exclusive. Children with an EHCP plan were nearly 8% more likely to be severely absent than average. Children in Y11 have twice the average rate.

All primary years, and a few ethnic groups have significantly lower severe absence rates.

The chart above shows relative severe absence rates of different groups, but we’ll complement that by quantifying the cohort of severely absent pupils in 2023 by their characteristics.

7.1 Severe absence - turnover and retention

It seems likely that there are children for whom severe absence is for some reason a persistent behaviour, and children for whom a severe absence happens in one or more years for some specific reason - like a crisis of health or personal circumstances. To try to understand this, we looked at year on year turnover and retention in the cohort of severely absent children.In the chart below, severely absent children are classed as retained if they were also severely absent the year before, and new if not. Both categories have risen in recent years:

So the problem of severe absence is, in part, due to a cohort we could describe as chronically severely absent.

The retention rate here is calculated as the percentage of all severely absent pupils in a given year that were also severely absent the year before. In secondary schools, in 2023, this was around 40% of children who were severely absent in 2023 were also severely absent in 2022.

This retention rate has risen in recent years:

Plotting the retention rate by NCY shows increased year on year retention as children grow older. Here we’ve included the NCY profiles of two years: 2018 and 2024, showing the increased retention rates across the board into 2024.

8 Daily attendance patterns

The analysis so far in this report has used data aggregated up to the half term or annual level. During the course of this project we processed the raw daily data (recorded as a string of symbols and codes) to allow analysis of attendance at the level of the individual day.

8.1 Week day

Fridays,(to a lesser extent Mondays) see significantly lower attendance than the other days of the week.

Looking at a time series, we see that Friday’s lower attendance is nothing new, and the gap has not really changed over time:

8.2 School attendance across the year

The day level data allows us to visualise an entire school year. Here we see how key points in the year and particular dates impact on school attendance. When the data are aggregated to the term level, there is very little seasonal variation, but differences at the day level are more dramatic than the differences we see between demographic groups.

In particular, we can see the impacts of:

• the first and last days of term
• a growing absence rates up towards Christmas
• a wave of teachers’ strikes
• heavy snowfall in March
• Eid
• the days immediately after bank holidays
• study leave
• increasing absence through the final summer term

Here is the same chart for the 2023-24 year:

Recreating the same plot for absences coded as illness (though this time showing the count of sick days rather than the % of available sessions) shows how rates increased dramatically through the run up to Christmas, peaks on Fridays (and to a lesser extent Mondays) throughout the year, and a significantly lower rate in the summer. There are also spikes in illness on the last day of each half term (except the summer). This is the plot for 2024 but the pattern is very similar in other years.

The day level no reason plot shows a similar shape to the illness plot. We could read this as suggesting that at least some of the no reason absences are explained by genuine sickness. Although the major spikes here on the last days of term may be due to unrecorded family holidays or other absences.

It’s worth comparing the 2023 and 2024 plots for no reason absences. As well as reduced levels of no reason absences throughout the year, 2024 sees much less seasonal variation - such as the steady build up to Christmas - although the end of term spikes are more pronounced.

9 Conclusion

School attendance is affected by a multitude of factors: age, economic deprivation, special educational needs, caring responsibilities, the culture of individual schools, the attitude of families and ultimately the children themselves. Factors associated with lower attendance are intersectional and compound each other.

The pandemic dominates the recent history of school attendance (and much else besides). COVID-19 lockdowns, social distancing and school closures were all surely transformative in cultural attitudes to school attendance, and the impacts were felt differently in different places. However, it would be a mistake to place too much emphasis on COVID-19 alone - deprivation & the cost of living; the rise of smartphones and social media; changes around special educational needs (both prevalences and attitudes) - these are all surely factors, many of which will have influenced one-another. Much of this is not recorded in the available data, and the interactions between these forces will be complex.

The good news is that despite the widespread risk factors identified here and despite recent social and cultural shifts, school attendance is recovering. Encouragingly, this recovery is strongest among the youngest cohorts of children. Recent changes to recording and the rules appear to be having an impact, but most inequalities persist, and some continue to widen. The coming years will tell if school attendance can recover to levels seen before the pandemic, and if the most vulnerable children can be helped to attend school as well as their peers.

This report is one of several produced under the inclusion & attendance data science project - there are also dedicted reports around Special Educational Needs (strategic needs analysis), the impact and effectiveness of services & interventions, and attendance by early years foundation stage attainment. Please refer to the links at the top of the SCC Data Science site for links to these.

If you have further questions about the data, analysis and narrative in this report please contact the Sheffield City Council Performance & Insight Team, or email giles.robinson@sheffield.gov.uk