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

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

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

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

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

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

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

Case study example ‘Undergraduate students who are required to withdrawn from university: The role of ethnicity’ British Educational Research Journal (2017)

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

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

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’

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

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

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

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

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

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

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

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

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

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

Withdrawal should be disaggregated Required to withdraw is distinct category

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

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

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?

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)

Put HEA report link here https://www.heacademy.ac.uk/node/10293

Any questions?

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

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

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’

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

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