Contextualising degree-level achievement: an exploration of interactions between gender, ethnicity, socio-economic status and school type at one large UK university

Abstract

Differentials in UK degree outcomes have been noted for some time, with research showing that not all types of students enjoy comparable levels of attainment, even when prior qualifications are controlled for. This article offers a new perspective by using regression models to examine interactions between the key variables that predict success among around 9000 students at one major UK university in terms of their chances of obtaining a ‘good’ (upper second or better) degree and a ‘first’ degree. As in previous studies, gender is found to be significantly influential, with female students’ attainment being superior to that of male students. However, significant interactions are noted between gender, ethnicity and socio-economic class indicators. These interactions are integral to developing a better understanding of what happens to equal-attainment students once they reach university, and they allow for a more fine-grained and accurate picture to emerge of the different factors that influence success. We interpret these interactions, along with school type effects, and discuss their potential implications for the ways in which university applicants are selected and university students are taught.

Keywords:

• Degree attainment
• gender
• ethnicity
• socio-economic status
• school type
• learning gain

Notes

1. Facilitating subjects are A-levels perceived to be ‘required more often than others’ for entry to higher prestige universities (Russell Group 2016/17).

2. Three other methods of measuring WP were tested and not found to be significant (or were found to be causing multicollinearity), so were removed from the models: household income, parental education level and low participation neighbourhood. School type and National Statistics Socio-Economic Classification were found to have low correlation – they were included in the final models.

3. Young students’ socio-economic data relate to the occupation of their highest earning parent, whilst for mature students the data relate to their occupation prior to registering at the university (see Richardson and Woodley 2003, 480–481).

4. This resulted in low numbers of students in the Joint Academic Coding System (JACS) codes of Medicine and Dentistry (code 1) and Education (code I), which were excluded from the analysis.

5. The average grade for General Studies A-level was just above a C (84.9 points) and the average for all other A-levels was just above a B (105.6 points).

6. Domicile and UK Domicile Area, as well as age at entry (young versus mature), were also tested but were not found to improve model fit, so they were not further used in the models.

7. Crawford’s (2014) list relates primarily to the likelihood of drop-out, but also has relevance to degree performance.

8. Analysis of drop-out rates within this case-study institution reflects the patterns found in the HEFCE (2014) study regarding school type (i.e. state school pupils were more likely to drop out than independent school pupils) but with a smaller gap.