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Research Sample Topic

Educational Inequalities in UK Universities: a big data approach


Educational inequality is an increasingly important issue in UK universities. This study utilises The Office for Students (OfS) underlying dashboard datasets for Attainment and Continuation, to make accurate comparisons between various gaps , alongside exploring the OfS claims that progress has been made towards closing a number of these gaps. (OfS, 2021). The term "gap" is defined as the differences between the outcomes of students with a specific characteristic and the outcomes of their peers. Python was used to carry out data cleaning and wrangling, enabling a range of visuals to be presented and analysed. The main finding from this study was that no significant change in Attainment Gaps or Continuation Gaps for nearly all demographic groups. There is also minimal internal correlation for both Attainment and Continuation with zero external correlation.


Educational inequalities are an increasingly important focus within Higher Education, with students, universities and university regulators demanding that all students have equal opportunity to succeed independent of background or demographic characteristic. A fair amount of literature has been published on gaps in education, these focus mainly on ethnicity, there is limited literature on gender, age and disability gaps. In 2013 Richardson et al, found that overall the odds of non-White students obtaining a good degree (1st or 2:1) was approximately 50% less compared to White students (Richardson, 2013). These findings were supported in 2015 when Mountfords-Zimdars (2016) study found that White students outperformed BME students between the years of 2010-2013 even though the number of students from black and minority ethnic (BME) backgrounds increased by 7%. Furthermore Mountfords-Zimdars found a link between ‘the lack of ethnic diversity among UK academies’ and ‘differentials in progression and attainment’ (Mountfords-Zimdars, 2016). Following on Richardson (2013) identified that older Black or Asian students were less likely to gain a good degree compared to younger students, this was also said to be the case for Female students. Richardson also found that the gap was greater in some subjects contrary to this Mountfords-Zimdars noted that their research did not take into account subjects studied. A more recent study carried out by Universities UK (2019) found BAME students to have a 13% attainment gap over white students from the same background. Much of the literature fails to clearly define reasons for these gaps. A case study carried out on 6 providers highlighted learning styles to be a contributing factor, with BME students and male students using surface learning approaches which is known to lower attainment. (Cotton, Joyner, George and Cotton, 2015). The research also considers assessment methods and gender suitability with coursework becoming more prevalent in universities which is known to suit female learning styles more than males (Cotton, Joyner, George and Cotton, 2015), as possible factors that affect the attainment gap. Despite the range of research into the differential degree attainment of BME students ‘no clear systematic effect, or combination of effects, has fully explained the difference. ‘ Sanders, J. and Rose-Adams, J, (2014) Tinto (1993) found that withdrawal rates were higher for students who were less integrated and factors such as social system and academic environment were contributing factors. Although this literature is not recent these factors appear to still be relevant in universities today and therefore could be impacting the continuation gap. In order to continue addressing these gaps, the OfS has made it a requirement for all educational institutions to submit an access and participation plan. With ‘Universities and colleges committed to investing a total of £551.8 million in 2020-21 to deliver the access, financial support, research and evaluation elements of the commitments detailed in their plans’ (OfS, 2021) The OfS will regulate providers using key performance measures to address and improve consistency for persistent sector wide gaps. From the OfS review of the plans, if all providers with targets meet them, the attainment gap between black and white students is projected to reduce from 22% to 11.2% by 2025 (OfS, 2021). This projection makes assumptions regarding the size of the graduating cohort. Similarly for students with disabilities the gap would close by 2.8%, which equates to an additional 750 disabled students projected to achieve a 1st or 2:1 in 2024-25 (OfS, 2021). With gaps existing at every level of education (Amadeo, 2020) it is important that the disparity in educational attainment and continuation is further analysed between gender, age, disability and ethnicity groups. Reviewing educational performance in UK universities has been made possible with the production of an online dashboard by the OfS (Office for Students, 2021). Sourced from national collections such as the Higher Education Statistics Agency (HESA) and the individualised Leaner Record (ILR) (OfS, 2021), the data separates student’s higher education journey into four stages; Access, Continuation, Attainment and Progression. While the BAME awarding gap has been studied for several years in UK and non-UK contexts, there has been less attention paid to awarding gaps for other demographic groups, including mature students and disabled students. There has also been relatively little systematic analysis of gaps in continuation rates across the sector, so important differences in the student journey are potentially being overlooked. This study will focus on the lifecycle stages Attainment and Continuation. The Attainment indicator is based on students awarded an undergraduate degree at ‘first’ or ‘upper second’ (2:1). Whereas the continuation indicator is based on student activity on a census date one year and 14 days after their commencement date’ (OfS, 2021). This study builds on the OfS’s analysis into gaps and contributes to their findings through performing statistical analysis on all providers who have a complete dataset for all 5 years. Although this will not be inclusive of all 366 providers it will enable valid comparisons to be formed when establishing the progress made towards narrowing the gaps in UK universities.

This study aims to address this knowledge gap by answering the following research questions about UK higher education:

  1. What are the gaps in continuation and attainment for gender, mature students, disability and ethnicity?

  2. Has there been any significant improvement in these gaps over the last 5 years?

  3. Is there any correlation between continuation and attainment gaps at the sector level?


Data Collection and Cleansing:

Raw data was obtained from the Office for Students website ( Office for Students, 2021). To answer the specific research questions outlined above, the data was filtered to only include relevant records. Using R Studio and the supporting package dpylr the LIFECYCLE STAGE was filtered to extract the Student lifecycle data for Attainment and Continuation only, this was then subsetted to only return gaps, these gap indicators were reported across a five-year time series. Finally the data was subsetted specifically on all undergraduate students who were full time or doing an apprenticeship, reducing the dataset from 81369KB to 2822KB. This generated a new dataset which was reviewed and cleansed prior to analysis.

The initial database used data up to years 2018-19 due to the amount of disruption to universities during the pandemic (2019/2020) that data is unlikely to be comparable. The database consisted of a ‘measuretype’ column whereby statistical significance was reported by ‘yes’ or ‘no’ rows. This data was discarded to establish a numeric dataset starting with 366 providers. Supressed data due to data protection (DP) was removed leaving 361 providers. Next rows containing N (providers with a population lower than 25) where removed, reducing the provider count to 357. Pythons Pandas Profiling was carried out to review and explore missing values. This found only two providers had data for 4 out of the 5 years and several providers where missing two or more years. Therefore to ensure consistent fair analysis and enabling comparisons to be made any provider with missing data in years 1-5 was removed leaving the final database with 217 providers.

The database used the labels split1 and split2 when comparing one demographic group to another. However with split1 and split2 containing 48 and 42 variables respectively. This study selected 6 key characteristics as shown in Table 1.

Statistical analysis

The cleansed numeric dataset was used to carry out a statistical analysis for both Attainment and Continuation. Pythons .Describe() function enabled the max, min and count values, along with the mean, std, lower and upper percentiles statistics for each of the 6 demographic categories to be collated and recorded as a new dataset. This information was visualised for each year using Seaborn’s boxplot, overlayed with a swarm plot in order to be able to effectively interpret the differences between years 1-5 to identify changes to the gaps.

To detect statistically significance between the means of each year (Zach, 2021) a KruskalWallis Test was used. A significance level of 0.05 (α) was used to determine significance.

To establish if there was a correlation between attainment and continuation, the mean of data years 1-5 was calculated for each demographic and appended to a new dataframe ensuring provider_name was assigned as the key. To ensure the data was comparable, providers with data for years 1-5 and all demographic categories were included leaving 77 providers. This was also visualised using Seaborn’s box/swam plot with attainment gaps and continuation gaps on separate plots. These plots show outliers due to the plot assuming a normal distribution, the whiskers range was set at 1.5 of the interquartile range (IQR). To explore outliers further, a Grubb test was carried out for all demographic categories, looking at both the max and min values applying a significance level 0.05.


Evaluating the Attainment and Continuation Gaps

To explore gaps across the sector, the size of continuation and attainment gaps at provider level was determined for the last 5 years of data. Firstly Attainment Gaps (AG)for all providers where analysed for each demographic for the years 2014-2019. (Figure 1)

For attainment, the average gaps were -4.90% for gender. 5.92% for mature students and 1.18% for disability (Figure 1a,b,c). There were no significant differences in gaps as a function of year, indicating no significant progress was towards reducing these educational inequalities. For ethnicity, the gaps were 21.39% for Black students, 13.26% for Asian and 6.72% for Mixed ethnicity (Figure 1d,e,f). There was a statistically significant difference in the Black attainment gap across years (S = 10.35, P = 0.03), indicating some progress has been made on this across the sector in the last 5 years, however this demographic has the largest AG by over 8 points.

The range of gaps for most demographics show several providers with a negative gap which means that split 2 performed better than split 1. This is most evident in figure 1a) validating that Females outperformed Males in 61 out of the 70 providers. Students with mixed Ethnicity (Figure 1f) have the smallest range, yet it is clear from the box plots that AG has a large spread in all years. The spread for students with a Disability (Figure 1c) is smaller meaning that providers are getting more similar results for these students compared to students with no known disabilities.

Figure 1: Sector Level Attainment Gaps across 5 years. Each data point represents one provider; only providers with complete data for all 5 years are presented, N represents this value. The box plots indicate the interquartile range with the median in the centre. The range is shown by the lines. 0 on the y axis indicates no attainment gap. (*) indicates statistical significance of P<0.05 in a Kruskal-Wallis test.

Next Continuation Gaps for all providers were analysed for each demographic for the years 2013-2018. (Figure 2). The average gaps were -3.01% for gender. 4.24% for mature students and 0.92% for disability (Figure 2a,b,c). The findings were the same as Attainment with no significant differences in gaps therefor no significant progress been made to reduce these educational inequalities. For ethnicity, the gaps were 2.02% for Black students, -0.37% for Asian and 1.92% for Mixed ethnicity (Figure 2d,e,f). There was a statistically significant difference in the Gender continuation gap across years (S = 10.08, P = 0.04), indicating some progress has been made on this across the sector in the last 5 years.

The Gender CG increased by -1.4% over the 5 years indicating the number females leaving university courses is continuing to increase compared to males. Gender and Age have a larger spread (Figure 2a,b) meaning that the number of students who withdraw from university varies for each provider. Interestingly students of Asian ethnicity (Figure 2e) are more likely to continue their studies over other ethnicities. Mixed ethnicity (Figure 2f) has a smaller range and spread of data compared to other demographics, indicating providers are recording similar number of students with Mixed ethnicity withdrawing.

Figure 2: Sector Level Continuation Gaps across 5 years. Each data point represents one provider; only providers with complete data for all 5 years are presented, N represents this value. The box plots indicate the interquartile range with the median in the centre. The range is shown by the lines. 0 on the y axis indicates no attainment gap. (*) indicates statistical significance of P<0.05 in a Kruskal-Wallis test

Table 2: Results of Kruskal-Wallis tests for differences in gaps across the five years of data (2014-2019). Results are presented with the most significant results first.

Having established that there was little change in gaps across the five years of data, the gaps were averaged to allow Attainment and Continuation to be compared across the 5 year time period (Figure 3). For attainment, the largest gap was for Black students 21.39% followed by Asian 13.26% and Mixed ethnicity students 6.72%. This pattern was not mirrored by the continuation data in which the largest gap was for Age 4.24% followed by Gender -3.01% followed by Black 2.02%.

Overall Attainment has larger gaps for all demographic groups than Continuation. Figure 3 does show potential outliers, however a Grubbs test identified only one outlier (-9.58%) in Gender continuation. This finding however does not impact the analysis.

Figure 3: Sector Level Attainment and Continuation Mean Gaps. Gaps were averaged across 5 years of data, and only universities with complete data for all 5 years are presented. A: Attainment Gaps (82 universities) and B: Continuation Gaps (75 universities). All box plots indicate the median in the centre, interquartile range by the box, range by the lines. The diamonds indicate a potential outlier in the results.


Correlation Internal Correlation has been carried out separately for Attainment and Continuation in order to explore the relationship between each demographic (Figure 4) (Joe and Mendoza, 1989). For Attainment the strongest correlation is Asian/Black 0.68 followed by Black/Mixed 0.46 and Asian/Mixed 0.43 having similar correlation. Although this indicates that if Black students do not make sufficient progress at one provider this will apply to Asian students as well. Linear Regression results recorded -0.147 for the R2 value, therefor if any prediction was to be made using this data the mean value would be more accurate (Nerdy, 2021). Gender has almost no correlation with any of the other demographic groups.

The correlation for Ethnicity in continuation is slightly less (Figure 4b), with Mixed/Black 0.5 having the highest followed by Asian/Black 0.44 and Asian/Mixed 0.42. Continuation also reveals another correlation group; Gender/Disability 0.48, Age/Disability 0.41 and Age/Gender 0.33. However with all correlation results been < 0.8 there are no strong positive relationships (Frost, 2021) between any demographics in Continuation or Attainment, however Attainment Asian/Black is >0.6 indicating a moderate correlation (Frost, 2021).

Figure 4: Sector Level a) Attainment and b) Continuation Internal Correlation Matrix. The average gap over years 1-5 for each demographic has been used to review the correlation. Pearsons Correlation Coefficient has been applied with pandas .corr function and the results visualised through Seaborn’s heatmap feature. The scale -0.6-1 represents negative correlation to very strong positive correlation respectively.

To identify correlation patterns, the two groups with the higher correlation scores for continuation where visualised (Figure 5) on the same plots. The scatterplot shows very few providers are close to achieving equal attainment or continuation rates for any of the demographic groups. Figure 5a shows clear distinct clusters of data for each demographic these appear to follow a similar trend, particularly the CG for Disability/Age and Disability/Gender groups. The negative CG for Gender and Age is clear with at least 3 data points that sit further away from the main cluster. The CG for all three Ethnicity groups are very similar (Figure 5b). The data’s spread is also comparable with several points over lapping or been very close.

Figure 5: Continuation Correlation Review. The demographics have been separated into the two higher correlation areas a) Age, Gender, Disability and b) Ethnicity and plotted using Seaborn’s scatterplot. The black line indicates where the demographics would be equal with no gap. Each demographic correlation is depicted using a different colour as shown in the figure legends.

External Correlation

Comparing AG to CG for each demographic enabled external correlation to take place (Figure 6) . No correlation was found between the different demographic groups when using Pythons .corr function for Attainment and Continuation, this evident by results been <=0.01. Figure 6 shows that for all demographic groups the gaps for each provider are spread out, with Age having a larger distribution of data and Disability having the smallest.

Figure 6: External Correlation Between Attainment and Continuation. For each provider (75) the mean of the 6 demographic categories for both attainment and continuation has been plotted using Seaborn’s scatterplot. corr shows the results of Pearsons Correlation Coefficient when applied using Pythons .corr function


This study aimed to determine what the continuation and attainment gaps have been across UK higher education for six different demographic comparisons (Gender, Age, Disability, Black, Asian and Mixed Ethnicity students). It found that with the exceptions of Black Attainment and Gender Continuation there has been no improvement in gender gaps over the last 5 years. The AG is larger for all demographics compared to CG. There is no correlation between Attainment and Continuation however internal issues between ethnicity seem to behave differently to Age, Gender and Disability particularly for Continuation.

These results contribute to the understanding of Ethnicity gap in UK universities. Evaluating 91 providers, Black students AG mean is 19.86% (2018-2019), this gap has narrowed by 2.19% in the last two years. In 2013 the ECU reported a gap of 6.3% when considering the likelihood of Black students continuing or qualifying, compared to White (ECU, 2013). A direct comparison of these two results cannot be made due to differences between continuation and continue/qualify, however fundamentally these gaps are too large and leads to questioning, if enough is been done by universities to have a sufficient impact on this demographic? Other demographics should also not be ignored, in 2018-2019 Asian students AG was 12.6%, although this has shown some reduction (1.18%) the gap increased from 2017-2018 showing inconsistency. With the OfS plans to eradicate almost all gaps by 2030, it’s paramount the OfS uphold their commitment to regulating individual providers to ensure continuous improvement for all student outcomes (OfS, 2021). If the rate of reduction does not improve the 2030 target will not be met.

An unexpected finding was the change in Gender CG, an increase of -1.4% makes this change significant with more females leaving courses. Comparing Hillman and Robinson (2015) findings which recorded 8% of males leaving university compared to 6% of females. There could be several reasons for this result; the calculation of the drop-out rate could be different. The number of female students could have continued to supersede males, or a larger range of data may have been used leading to differences in results. However for the 191 providers in this study, this is an educational gap which needs addressing. The Gender AG is a well-known issue and the results confirm that between 2014-2019 no significant improvement has been made. Hillman and Robinson (2015) offer several potential reasons/strategies to reduce this gap, from looking at cognitive differences, to encouraging foundation years for males and even the impact of been taught by a male or female teacher. From this its clear more needs to be done to explore what universities are doing to reduce these gaps and review the effectiveness of these strategies.

Some limitations need to be considered; due to the datasets fluctuation of numbers for providers in each year, this study was unable to evaluate the demographics individually for each year. Therefor recommendations have been made on a sample population of providers. The subjects studied are also not taken into account, delving further into this area would offer insight into where issues lie, leading towards practical solutions to narrow these gaps.

A further study could include reviewing the number of students for each demographic group, this study was unable to review this data. Evaluating the proportion of the demographic groups for each provider would offer further conclusions. Aspects such as full time/ part time and type of providers could be compared with Richardsons (2013) findings that the AG for Black/Asian was larger for part time students and ‘new’ universities. The data structure in this study does not allow for intersectional (Crenshaw, 1989) analysis and therefor no conclusions can be made on students with one or more characteristic.

In 2017-2018 the AG for Disabled students (123 providers) was 2.7%, a study by St Andrews University (2019) reported the AG as 1.9% although this is lower both results are lower compared to other demographics. The Disabled students CG is even lower with this report recording 0.96% (2017-2018), similarly St Andrews University recorded a 1% CG. St Andrews created an improvement action plan by comparing its individual results to the sector as a whole. With strategies including appointing a Research Fellow and Project Manager in Equalities, carrying out a curriculum audit (University of St Andrews, 2019) as well as improving the support given to individual students with disabilities.

These initiatives could lead to a reduction in educational gaps depending on the execution. Mountford-Zimdars et al (2016) ‘highlighted that initiatives work best when they are embedded rather than bolted on, when they combine bottom-up and top-down approaches and partnerships between senior managers, students, and academics and professional service staff.’ This literature shows universities are thinking about strategies to improve outcomes for all groups of students, however it’s not clear how well these are been implemented. It must be noted that this is a sector wide issue, yet the potential solutions are from individual providers, the results from this study show no significant improvements in gaps, indicating more needs to be done to ensure providers work together and collaborate on solutions.

Finally the results show that there is a large disparity in the gaps for providers in this study. Therefore it would be valid to establish what providers with smaller gaps are doing differently, to enable other providers to utilise practises from other providers who are improving outcomes for students with different characteristics.


This study found CG to be lower than AG for all demographic groups analysed. These gaps effect a significant number of students particularly mature students and female students who are more likely to withdraw from university compared to young students and male students. Black (21.39%), Asian (13.26%) and Mixed students (6.72%) are significantly less likely to achieve a good degree compared White students.

An important finding is that there is no external correlation between AG and CG in UK Universities. However internally there is some correlation, therefore a university with a large Attainment or Continuation gap for black students is more likely to have one for Asian students. However this is generally speaking, the results do not have enough statistically significance to distinguish this as a rule.

From the correlation figures and visualising the data it is important for universities to take note of the two clear groups for intervention. Although all ethnicity groups should continue to be a focus for Attainment and Continuation, with each ethnicity having similar data points, universities could try the same approaches for all 3 groups. At least one more approach is needed to tackle the Age, Gender and Disability gap. Ideally due to the set clusters universities should alter the approach for each group.

The fight for equality in education is continuing to strengthen. With this effecting the futures of so many students. Whether students complete their studies or not and what degree they are awarded will have implications for students job prospects and financial situations moving forward. Looking at the wider picture for some of these gaps, if Gender AG was reduced to zero this would lead to approximately 33,834 extra students obtaining a good degree based on the HE enrolment figures (HESA, 2021), this is the same for Black students with 23,264 effected. Finally if Age GC was reduced to zero then 25,739 more mature students would complete university. It must be highlighted that there will be a crossover of students in these results if they have more than one characteristics, however such high numbers in all demographics strengthens the need for change and the obligation for this to be tackled at a sector level for all students.


1. Amadeo, K., 2020. How the Educational Achievement Gap Affects Everyone. [online] The Balance. Available at: [Accessed 1 August 2021].

2. Crenshaw, Kimberle (1989) "Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics," University of Chicago Legal Forum: Vol. 1989 , Article 8.

3. Cotton, D., Joyner, M., George, R. and Cotton, P., 2015. Understanding the gender and ethnicity attainment gap in UK higher education. Innovations in Education and Teaching International, 53(5), pp.475-486.

4. ECU (2013) Equality in higher education: statistical report 2013. London: ECU

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