1) The study aimed to examine factors associated with differential attainment between international and domestic distance learning students in a postgraduate public health program. 2) Preliminary results found that age, gender, and ethnicity were predictors of grades, with younger students, females, and white, Asian, and Chinese students achieving higher grades. 3) Region of birth was a better predictor than region of domicile or nationality, and language of first degree was significant for GPA. However, more work is needed to analyze education and work experience data.

1. Dr Isla Gemmell
Senior Lecturer in Epidemiology and Statistics, School of Health Sciences
Dr Roger Harrison
Senior Lecturer in Public Health, School of Health Sciences
Determining factors associated with
differential attainment among transnational
distance learning students

2. Postgraduate programmes in Public Health and
Primary Care
Around 250 students registered on our programme. Approximately 90%
study part-time completing the programme in 3-5 years
Around 75% pay home/EU fees and
25% pay international fees.
Mainly mature students, around
50% are aged 25-34 and 30% aged
35-44

3. 2015 Cheril project: Why do non-EU/UK distance
learners have lower grade attainment?

4. Differential attainment on MPH programme
Home/EU
International

5. Aims of the 2015 project
• To describe Grade Point Average (GPA) and degree attainment for EU (i.e.
including UK) and transnational (international) students on a postgraduate
distance e-learning (DL) programme in public health
• To identify pre and post-admission predictors of GPA and degree
attainment for UK/EU and transnational students
• To examine the relationship and impact of students’ cultural & contextual
(professional and personal) backgrounds with their learning experience
and identify innovative opportunities to deliver an outstanding learning
experience for postgraduate DL students regardless of geography

6. Results (as presented at CHERIL Conference Dec 2015)
Quantitative data
As anticipated, identifying, obtaining, and linking different
datasets into a working file is challenging
Problems include:
oSourcing different datasets across the University is challenging
despite the wealth of collected student data
oA lack of standardised approaches to variable formats
oKey variables embedded within text strings
A number of key datasets have now been obtained and work is
on-going to develop these into workable files ready for analysis.

7. Example admission form

9. Data received from admissions
• 7 separate Excel files
• Multiple records per student
• Mostly text based responses
• No drop down menus on electronic form
• No coding system
• Language of instruction of first degree not ‘first
language’

10. • Academic programme
• Academic plan
• Year of entry
• Date of birth
• Gender
• Fee paying status
• Country of domicile
• Country of residence
• Nationality
• Ethnicity
Discoverer data
• EBP grade
• Biostatistics grade
• FoE grade
• Dissertation grade
• GPA
Programme data

11. Data for analysis
• 705 students registered on our programme between
2010 and 2015.
• 606 have at least one grade score
• Only 5% of our students did not have English as
language of instruction of first degree
• Students came from:
• 53 different countries of domicile
• 60 different nationalities
• 82 different birth countries

12. Region of birth, nationality and domicile
Region Number of students
Country of Birth Country of Nationality Country of Domicile
UK 309 383 440
Europe 73 79 63
Asia 49 27 19
North Africa & Middle East 26 9 7
Sub-saharan Africa 210 163 128
North America & Oceania 26 32 40
Latin America and the
Caribbean
10 4 5
Missing 2 8 3
Total 705 705 705

13. Results of multiple regression analysis
• Age, gender and ethnicity were the main predictors of grade
for each of the three course units and for GPA
• Region of birth was a better predictor than region of domicile
or nationality but not as strong as ethnicity
• Language of instruction of first degree was statistically
significant for GPA but not the three course units
• Younger students, females and white, Asian & Chinese
students achieved higher grades
• We are still working on getting the education and work
experience data into a suitable format for analysis

14. Conclusions & Recommendations
• The university collects an extensive amount of data on our
students mainly for administrative purposes.
• It is difficult to access, use and analyse these data at
programme level
• Appropriate analysis of this data could be used to inform
programme development and targeted student support
• Specific mechanisms could be developed to enable
programme directors to access these datasets and standard
coding systems could be used.