Finding the Vicars Daughter

Finding

The

Vicars Daughter

Overview

• Educational Disadvantage• Contextual Data and Applications• Current Research Findings• Challenges• Conclusion

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

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.”

Information for Targeting & Contextual Data

Indices of Multiple Deprivation / IDACI

Low Participation neighbourhood

State school / college

Disabled In care

No parental HE

NS SEC 4 to 7

Low income backgrounds

Schools/ CollegesDfE/Data Service

Free School Meals (Pupil Premium)

16 – 19 Bursary

Ethnicity

Gender

Data routinely collected

Not routinely collected

Targeting “Disadvantage”

Gathered by (or can be derived from) UCAS…good for baselines, target setting, tracking and evaluation

HEIs own criteria

SEN or AP+ or AP?

In careGeodemographics

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

Identification of SESSocial – 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?

University of Liverpool (2010-2011): Proportion of students and their degree

classification in relation to NS-SEC

I II-1 II-2 III PASS0

10

20

30

40

50

60

NS-SEC 1-3NS-SEC 4-7Unknown

%

“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.“OFFA fines????

Fees capped at £6,000????!!!

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

ORA

• Eligible for FSM OR

•Receipt of 16-19 Bursary OR

•School Performance

B

•No Parental HE or

•Parents in manual / semi skilled occupations or unemployed (NS-SEC 4-8)

•Deprived Postcode (IMD, LPN, ACORN) OR

C •Disabled OR

•In care

Flexible and cost-effective approaches:•Use baskets of measures for macro-targeting to avoid vicar’s daughter and overcome limitations of singular measures and statistics

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- EngineeringA- ArtsV- Veterinary Sciences

S- ScienceS& E- Social and Environmental SciencesM- Medical Sciences3yrs- All 3 year degree programmes combined

UoL: Percentage of Students in Lowest and Highest Quintiles by IMD 2004/5-2009/10

E A V S S&E M 3yrs 4yrs

Quintile 1& 2

35.2 25.1 18.8 28 23.2 20.2 25 27.5

Quintile 4 & 5

49.4 48.2 62.3 44.9 50.1 48.7 47.5 44.6

E- EngineeringA- ArtsV- Veterinary Sciences

S- ScienceS& E- Social and Environmental SciencesM- Medical Sciences3yrs- All 3 year degree programmes combined

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

• How/when are we going to monitor the data and related audit?

• How are we going to assess progression and retention?

• Stage 2–

Interviews/Focus Groups

• How/ when will we assess what difference we have made? (5 or more years?)

• How are we going to ensure that we take into account Demographic Changes?

Conclusion-The ChallengesWhat 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 MeasuresHow can data be “sourced once and used many times”/ what are the resource implications?– Collaboration with Universities?

References & Useful Sources

• https://www.zotero.org/tammyt/items/• http

://www.maptube.org/map.aspx?s=DBHFOjWDOMsqgol5yWDAp1wcCnY8CghN

• 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 Sta

tes federal government standards agency.• Geographic Information System (GIS) Educational

website—Educational site with PDF lessons and videos to accompany free GIS software.