1. Finding
Finding the Vicars
Daughter
The
Vicars Daughter
2. Overview
• Educational Disadvantage
• Contextual Data and Applications
• Current Research Findings
• Challenges
• Conclusion
3. Educational Disadvantage
• Social class remains the strongest predictor of educational
achievement in the UK
• Children from low SES slower at acquiring language , less
proficient at mathematical tasks and gain significantly poorer
paper qualifications (Aikens & Barbarin, 2008, Coley, 2002;
Chowdry et al., 2009)
– PISA (2009) gap between the mean scores of UK students in the 90th
and 10th percentiles was 246 points – the equivalent of six years of
schooling on the average
4. Impact of Educational Disadvantage
• Hoar & Johnston (2008) school grades do not
represent true ability
• In England students from the highest social class
groups are three times as likely to enter
university than those from the lowest social class
groups.
• “ Graduates on average have better employment
prospects and can expect to earn at least
£100,000 (160,000 gross) net of tax, more than
non-graduates across their working lives.”
5.
6. Targeting “Disadvantage”
NS SEC 4 to 7
Data routinely collected
Schools/ Colleges Low Participation
neighbourhood Not routinely collected
DfE/Data Service
Free School Meals
(Pupil Premium)
Indices of Multiple Deprivation / Gathered by (or can be
16 – 19 Bursary IDACI derived from)
SEN or AP+ or AP? UCAS…good for
baselines, target setting,
In care
No parental HE tracking and evaluation
Geodemographics
Disabled In care
State school / college
Low income backgrounds
Information for
Targeting & Contextual Data
Ethnicity
Gender
HEIs own criteria
7. Practical Usage of Contextual Data
• University of Bristol
• Priority is given to students in the following categories: Local students (resident in BA or BS
postcodes)
• Those who are part of the first generation in their family to go to university;
• Those living in a Low Participation Area (LPN).”
• Research Cluster (Hoare, 2009), justify admitting students with between one to two grades lower (for
typical AAA offers) and three grades lower (for ABB offers).
• University of Birmingham
• Basket of measures- Family Education (little or no experience of HE) (1), Parental Occupation- based
on household income (£42,600 or less) (2), Post code data, school/college/area rates of progression to
HE (3), teacher recommendation of application (4), non- selective state school or college (5)If at
selective state school the school where you did your GCSE’s must have achieved less than 49% A*-C
grades at GCSE
• University of Manchester
• Flagged based on following criteria: Performance of school at level 1 ( GCSE or Equivalent) or Level 3 (
A level or equivalent) – flagged if school performs below national average, post code data ( use ACORN
and LPN) , care or disabled.
• Overall contextual flag is produced if you meet at least one of the social/educational indicators plus
the postcode indicator. You will also receive an overall flag if you have been in care for more than
three months.
– Geo-demographic indicator – An online postcode look-up facility
– Educational Indicators (PDF document, 2 MB) – a list of schools and contextual flags
8. Identification of SES
Social – Geo-demographic indicators of disadvantage and low
progression to HE USED by most Universities
• CACI ACORN provides the smallest granulation of analysis, on the
full postcode with detailed descriptors for each type
• ACORN data consists of 5 categories, 17 groups and 56 types.
• HEFCE POLAR2 data assigns all electoral wards into quintiles based
on progression to HE. Those wards in the lowest quintile according
to HE progression are classified as Low Participation
Neighbourhoods.
• NS-SEC :
– Information obtained directly from UCAS application form
– Unknown data ~ 25%
– NS-SEC 8 not included
– Who categorises the labels?
9. University of Liverpool (2010-2011):
Proportion of students and their degree
classification in relation to NS-SEC
60
50
40
NS-SEC 1-3
30
% NS-SEC 4-7
Unknown
20
10
0
I II-1 II-2 III PASS
10. “Analysis based on NS SEC 4-
7 excluded students from
benefit-dependent
families, and Aimhigher was
targeting those families,“
“Participation rates from
students receiving free school
meals rose from 13 per cent in
2005 to 17 per cent in 2008.”
"Aimhigher was busy
increasing applications, but a
lot were not able to get
university places… there was a
big excess of demand over
supply”
Aimhigher targeted children as
young as 13, so many of these
pupils have not even applied to
university yet.“
11. Current Research
– PILOT PHASE
– Stage 1 Analysis INCLUDES ALL degree
programmes between 2004/5 and 2009/10 at UoL
– ISSUES-Old Faculties (How can we map on to new
faculties?), data difficult to obtain, limited to
those that completed degree programmes
– However, findings from stage 1 highlight
meaningful predictors and differences between
faculties
12. Flexible and cost-
effective approaches:
•Use baskets of
• Eligible for FSM OR
• Receipt of 16-19 Bursary OR measures for macro-
A • School Performance targeting to avoid
vicar’s daughter and
• No Parental HE or
overcome limitations of
• Parents in manual / semi skilled occupations singular measures and
or unemployed (NS-SEC 4-8)
B • Deprived Postcode (IMD, LPN, ACORN) OR statistics
OR
• Disabled OR
• In care
C
13. UoL - Percentage of Students in Each of the Faculties and for
Combination of ALL undergraduate 3yr programmes 2004/5-2009/10
E A V S S&E M 3yrs
Male 84.5 42.3 23.2 38.9 44.6 23.4 41.4
Ethnic Minorities 18.3 6 8.7 10.3 8.2 8.2 8.5
26+ 7.7 3.5 0 2.3 2.5 11.2 3.6
Disability 2.8 6.6 8.7 6.1 6.4 8.2 6.5
E- Engineering S- Science
A- Arts S& E- Social and Environmental Sciences
V- Veterinary Sciences M- Medical Sciences
3yrs- All 3 year degree programmes combined
14. UoL: Percentage of Students in Lowest and Highest
Quintiles by IMD 2004/5-2009/10
E A V S S&E M 3yrs 4yrs
35.2 25.1 18.8 28 23.2 20.2 25 27.5
Quintile
1& 2
Quintile 49.4 48.2 62.3 44.9 50.1 48.7 47.5 44.6
4&5
E- Engineering S- Science
A- Arts S& E- Social and Environmental Sciences
V- Veterinary Sciences M- Medical Sciences
3yrs- All 3 year degree programmes combined
15. Potential analysis plan
We are interested in what predicts
outcome (final % score or degree
classification)
Variables we are looking to explore;
UCAS entry score
IMD (as a proxy for SE status)
State school performance
Gender
16. • How/when are • How/ when will we
we going to assess what difference
monitor the data we have made? (5 or
and related more years?)
audit?
• How are we going
to assess
progression and
retention? • How are we
going to ensure
• Stage 2– that we take
Interviews/Focus into account
Groups Demographic
Changes?
17. Conclusion-The Challenges
What do we need to know:
• To target effectively?
– What indicators to include?
What data do we need, who holds it, and what
do we need to collect?
– SPA suggested Basket of Measures
How can data be “sourced once and used many
times”/ what are the resource implications?
– Collaboration with Universities?
18. References & Useful Sources
• https://www.zotero.org/tammyt/items/
• http://www.maptube.org/map.aspx?s=DBHFOjWDOMsqg
ol5yWDAp1wcCnY8CghN
• Association of Geographic Information Laboratories for
Europe (AGILE) – promoting academic teaching and
research on GIS at the European level
• Cartography and Geographic Information Society (CaGIS)
• Directions Magazine – All Things Location
• Federal Geographic Data Committee—United States federal
government standards agency.
• Geographic Information System (GIS) Educational website—
Educational site with PDF lessons and videos to accompany
free GIS software.
Editor's Notes
key
Neighborhoods, Ethnicity and School Choice: Developing a Statistical Framework for Geodemographic Analysis (2007)Richard Harris Æ Ron Johnston Æ Simon Burgess Popul Res Policy Rev (2007) 26:553–579 DOI 10.1007/s11113-007-9042-9 Applying GIS Technology and Geodemographics to College and University Admissions Planning: Some Results from Ohio State UniversityAn example of the use of geodemographics in examining access to education in the United States. The article was written by Professor D Marble and Mr V Mora of the Ohio State University.Additional keywords: Academic USA Research Education Geodemographics PRIZM TIGERgis.esri.com/library/userconfAccess to Higher Education 1991-98: 'Using Geodemographics'A reproduction of an article published in the journal Widening Participation and Lifelong Learning by David Tonks of Lancaster University, in which UK university entrants are analysed by their geodemographic profiles.Additional keywords: Academic UK Research Education Geodemographicswww.staffs.ac.uk/journal/volonetwoCACI LimitedAcorn, the first geodemographic classification in the UK, is produced by market analysis company CACI. The company provides a combination of data, software and consultancy to help market products and services more effectively to the right consumers.Additional keywords: Commercial Biz UK ACORN Geodemographics Lifestyles Databaseswww.caci.co.ukPopul Res Policy Rev (2007) 26:553–579 DOI 10.1007/s11113-007-9042-9 Neighborhoods, Ethnicity and School Choice: Developing a Statistical Framework for Geodemographic Analysis Richard Harris Æ Ron Johnston Æ Simon Burgess Archer L, Hutchings M, 2000, ``'Bettering yourself '? Discourses of risk, cost and benefit in ethnically diverse, young working-class non-participants' constructions of higher education'' British Journal of Sociology of Education 21 555 ^ 574Archer, L, Hutchings (2000) Bettering yourself, discourse of risk, cost and benefit in ethnically diverse, young working class non participants. Conway, C., Coombes, M. and Raybould, S. (2002), Participation in Further and Higher Education across the North East Region: A Benchmark Analysis of Recent Patterns, Centre for Urban and Regional Development Studies, University of Newcastle, Newcastle upon Tyne. Croot, D. and Chalkley, B. (1999), “Student recruitment and the geography of undergraduate geographers in England and Wales”, Journal of Geography in Higher Education, Vol. 23 No. 1, pp. 21-47. • Clare Holdsworth (2006) 'Don't you think you're missing out, living at home?' Student experiences and residential transitions. Sociological Review,(),pp.495-519 • Jackie Patiniotis and Clare Holdsworth (2005) 'Seize that chance!': Leaving Home and Transitions to Higher Education. Journal of Youth Studies,8(1),pp.81-95
Deprivation in Liverpool from public profiler. Also possible to show peoples grades according to location, Relates to AAB.Medicine, knowing how to widen participation will help you know how to do it for every subject,