1. Undergraduate Retention & Attainment across the Disciplines
Ruth Woodfield, School of Management
8th February 2017

2. Context HE participation has increased significantly in past decades
And retention and attainment levels improved considerably
However, students from diverse backgrounds persist in HE at different rates
Students from diverse backgrounds also attain at different rates

3. Disciplines – student body variation
Student body varies significantly across disciplines
And students bring a variety of characteristics with them to study that increase their vulnerability to withdrawal and/or lower attainment e.g. being:
a man
from some BME backgrounds
from lower socio-economic background
older
part-time
attending a more ‘local’ HEI
But disciplines also contribute to different patterns of retention, both independently and when interacting with student characteristics

4. Gender Gender profile varied: Biols Built Envt Comp Sci Engineering
Biols
Built Envt
Comp Sci
Engineering
GEES
Maths/Stats
Med/Dent
Phys Sci
Nursing
Philosophy & Rel Studies
Sector
% Men 43 71 83 86 54 60 62 12 46
% Women 56 29 17 14 40 57 38 88

5. Age Age profile also varied: Biols Built Envt Business & Management
Biols
Built Envt
Business & Management
History
GEES
Maths/Stats
Med/Dent.
Nursing
Philosophy & Religious Studies
Sector
% Traditional 77 66 60 65 78 75 80 24 59 40
% Mature 23 34 35 22 25 20 76 41

6. Mode of study
And mode of study varied, along with socio-economic class, ethnicity, local/not local students, UCAS points etc.:
Biols
Built Envt
Business & Management
History
GEES
Maths/Stats
Med/Dent
Phys Sci
Nursing
Philosophy & Religious Studies
Sector
% Full-time 85 71 66 68 82 77 99 88 55 67 69
% Part-time 15 29 34 32 18 23 1 12 45 33 31

7. Relationship between student body and discipline
Needs considering when assessment retention/attainment patterns, although causation complex
For instance:
computer science (9% leavers): more male, local, part-time – is this explanation?
Biols
Built Envt
Business & Management
History
GEES
Maths/Stats
Med/Dent
Phys Sci
Nursing
P&RS
Sector
% Complete degree 94 97 96 99
% Leaves without degree 6 4 3 1 7

8. Case study example
‘Undergraduate students who are required to withdrawn from university: The role of ethnicity’
British Educational Research Journal (2017)

9. BME students and exclusion from education
BME students generally more likely to be excluded from education at the primary and secondary levels of education (Runnymede Trust 2002; EHRC 2010, 2015; DofE 2012)
No work exploring whether this extends into higher education

10. What is exclusion in the HE context?
Different categories of withdrawal
Normally retention understood mainly as failure to ‘persist’ (Tinto 2006)
Here focusing on withdrawal without any qualification or without the degree qualification that they were intending to study for and where that is ‘non-voluntary’ as they are ‘required to withdraw’
2 reasons:
Academic failure
Breaking institutional rules

11. Student deficit models
Literature summary – underlying causes of identified pattern of increased withdrawal/academic underachievement in BME students
Student deficit models
From interview data from HEI staff (e.g. Jacobs et al. 2007; ECU/HEA 2008)
Students have ‘less prior attainment’, students ‘more disengaged’

12. Continued… Sector-side deficit models
e.g. staff/majority student attitudes; excluding practices result in ‘minority’ experience; ‘white curriculum’ etc.(ECU/HEA 2008; Stephenson 2012)
Richardson (2004, 2008, 2015) has pointed to ‘contingent nature’ of ethnicity attainment gaps – they change; can be context-specific - pointing to ‘intrinsically social nature’ of underlying causes

13. Overview HEA project on 2011 HESA data
Only students taking single-discipline programmes
Only English HEIs
Ethnicity categories - different levels of granularity depending on analysis

14. Percentage of students from available ethnic categories who left their course without attaining their degree
% of students in each ethnic group leaving without degree
Black or Black British - Caribbean 10
Black or Black British - African 9
Other Black British 11
Asian or Asian British - Indian 5
Asian or Asian British - Pakistani 7
Asian or Asian British - Bangladeshi
Chinese
Other Asian
Other Mixed
White

15. Student leavers only: reason for withdrawing, by ethnicity
% Complete a lower course
% for health
% for finance
% for ‘Other personal’
% for ‘Other’
% written off after time lapse
% left for paid work
% failed academic
% excluded
% Total Required to withdraw
All leavers 16 3 2 24 14 5 30 35
Black or Black British - Caribbean 17 9 8 1 40 47
Black or Black British - African 15 11 7 45 56
Other Black British 4 12 6 38 52
Asian or Asian British - Indian 19 41 44
Asian or Asian British - Pakistani 18
Asian or Asian British - Bangladeshi 22 48
Chinese 13
Other Asian 10 49 54
Other Mixed 20
White 27 29

16. Composition of student body across paired, ‘cognate’, disciplines, by broad reported ethnic category
% ‘White’
% ‘Black or Black British’
% ‘Asian or Asian British’
% ‘Other Mixed’
Sector as a whole 79 7 10 4
Social Science
Business & Management 72 14 5
Education 86 6 3
Science
Biological Science 77 12
Psychology 81 8

17. Reasons for student leavers to withdraw, by paired disciplines
% Completing lower course
% for health
% for finance
% for ‘Other personal’
% for ‘Other’ reasons
% written off after time lapse
% for paid work
% failed academically
% excluded
% Required to withdraw
Sector as a whole 16 3 2 24 14 5 30 35
Social Science
Business & Management 20 15 6 34 7 41
Education 17 4 29 21 25
Science
Biological Science 22 31
Psychology 26 11 32

18. Predicting likelihood of being required to withdraw as against voluntary withdrawal
Factors considered: gender, age, socio-economic class, parental HE, mode of study, distance, HEI type, ethnicity

19. Business & Management vs. Education results
B&M: 3 variables predicted likelihood of being required to withdraw:
Gender, age, ethnicity (strongest predictor, when controlling for others)
Black students over 4 times more likely to be R2W than White students
Asian students over 3 time more likely to be R2W than White students
‘Other mixed’ students were over 2 times more likely to be R2W
Education: model did not achieve statistical significance

20. Biological Sciences vs. Psychology results
Biols: 3 variables predicted likelihood of being required to withdraw:
HEI type, localness, ethnicity (strongest predictor when controlling for others)
Black students nearly 4 times more likely to be R2W than White students
Asian students over 5 time more likely to be R2W than White students
‘Other mixed’ students were over 2 times more likely to be R2W
Psychology: 3 predictor variables: gender, mode of study, localness

21. Key specific findings
All broad groups of BME students more likely to be required to withdraw from their studies
In the case of ‘Black or Black British – African’, ‘Other Black British’, ‘Other Asian leavers’, the majority of students are required to withdraw
Suggesting a continuation of exclusion trend at primary and secondary education levels
Elevated levels of R2W are offset by lower levels of leaving for ‘personal’ reasons
Elevated levels sometimes in disciplines with higher levels of R2W but cannot explain the independent role played by ethnicity

22. Withdrawal should be disaggregated
Required to withdraw is distinct category

23. Key general findings
Disciplinary context is a key mediating factor between socio-demographic characteristics and HE experience, attainment and retention
How similar students fare varies across disciplines
This cannot all be explained with reference to background characteristics that students bring into HE
Reasons for leaving are clearly partly a function of disciplinary customs and characteristics

24. Continued…
How student characteristics interact with disciplinary cultures, customs and practices needs more careful exploration
There is more value in sector-side focus than deficit models
We can do something about this side

25. Key emergent questions
Is the nature of the student experience as inclusive and consistent as it might be across disciplines?
Are we using available data as well as we could be?
Do we need more data?
Who takes responsibility to monitoring experience/attainment/retention of different groups and of different disciplines?

26. Final word “Access without support is not opportunity” (Tinto 2008)
“Higher education must accept that the implications of offering access to non-traditional students do not end, but rather begin, at the point of entry” (Bamber and Tett 2001)

27. Put HEA report link here

28. Any questions?

29. How do we support completion?
Focus on First year
Most students withdraw in year 1
Estimated 25-45% of students claim they considered withdrawal in Year 1
Most students cite very early experiences as key in their persistence and satisfaction
Research shows sense of ‘belonging’, fostered through ‘engagement’ is key to building early and lasting student satisfaction
Most effective strategies start pre-entry (‘preparedness’) and/or on Day 1

30. Belonging and Engagement
Belonging – students cite a sense of belonging to the course, department, school or university as very important
Some ‘non-standard’ students (e.g. BME; Local; Mature; Part-time) find this more difficult to establish
Research indicates belonging is established through engagement
With staff; peers; AND academic work
Lack of sense of belonging comes second, as reason for withdrawal, only to disappointment with course

31. Academic Engagement
Students cited “stimulating”, “interactive”, ‘real world”, “relevant”, “problem-based”, “enthusiastic” learning and teaching as key
Diverse learning opportunities & assessments
Supportive relationships with staff and fellow students around learning also key
Accessible staff – module tutors, personal tutors etc.
Prompt responses to contact
Clear and transparent criteria for assessments and prompt and constructive feedback
Peer learning and assessment opportunities
An ‘academic home’

32. Monitoring Monitor: Engagement Attendance and assessment submission
Identify at risk students and at risk times for withdrawal
Retention and completion – consider exit interviews/focus groups/surveys
Monitoring data regularly – hard even at national level data patchy and reporting differences suggested e.g. Vet Med no exclusions
Ensure data are high quality and utilised fully
Strategies are more effective if underpinned by data

33. Key issues for consideration
Mismatch between knowing and doing
Mainstreaming message
Not a minority issues: The majority of initiatives help the majority of students
One size does not fit all so vary offer
Think about presentation of initiatives
Listen to students
Be proactive in support and keep it going
Leadership
Managers/Leaders to show commitment
To recognise and reward capacity in staff