transforming education through data

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Transforming education through data Frank Bowley DfE

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Transforming education through data

Frank Bowley

DfE

Information and Education

The single largest barrier to a more effective and efficient education

system is a lack of information

Questions with little evidence

What is risk of non-payment on this

student loan?

Are teacher qualifications key to be a good teacher?

How much should we pay for a plumbing

apprenticeship?

What course should I do?

Which institution

should I attend?

Using Microdata: Benefits from Education

Questions we can begin to answer from use of microdata:

• Benefits to the individual and the economy

• Targeting of resources

Justify continued funding for FE

• Funding of apprenticeships and setting the levy

Using outcomes in performance measures

Replacing expensive surveys with poor response rates

The LEO Project

Matching data from a range of admin datasets to provide a

complete education and labour market record of individuals

Current database contains most people under the age of

around 30 years old and mature students who have been in

the publicly funded education in the last 10 years

The resultant dataset is:

Large (tens of millions of people)

Longitudinal – look at

Based on individual data

Using admin data for accountability 6

• 82% learners had a sustained positive

destination, into either employment or

learning, one percentage point higher

than in 2011/12.

• 72% were in sustained employment, of

which 15% were in also in sustained

learning, one percentage point lower

than in 2011/12.

• 25% were in sustained learning, of which

15% were in also in sustained

employment, one percentage point lower

than in 2011/12.

57%

10%

15%

82%sustained positive

destination

Employment only

Learning only

2010/112011/122012/13

Employment & Learning

Government proposing to rank colleges by their performance at getting

learners into work or more advanced education

7

Using admin data for accountability II

7

30%

40%

50%

60%

70%

80%

90%

100%

Sust

ain

ed

Po

siti

ve D

est

inat

ion

Rat

e

Providers ranked lowest to highest

Health, Public Services and Care

Business, Administration and LawLowest 10% of providers

Relatively high success rates but significant variance between providers

Admin data to research value of skills I

Admin data to research value of skills II

10

Total value

per student

(£000)

Per pound of

government

funding (£)

Total value per

year (£bn)

Level 2 Apprenticeship 61 26 12

Level 3 Apprenticeship 88 28 10

Full level 2 66 21 28

Full level 3 - loans 67 21 4

Full level 3 - grant 68 16 5

English and maths 14 17 7

Below level 2 7 10 5

TOTAL 34 20 70

Admin data to research value of skills III New value for money estimates, based on admin data analysis, shows that

FE skills have significant positive returns

Admin data to research value of skills IV

11

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%Income distribution 5 years after achieving a Full Level 3 qualification

Lower quartile

Middle 50%

Upper quartile

Next step will be to provide greater understanding not just on the average

return but also the distribution

12

Source: Brittan, Dearden, Shepard and Vignoles (2016)

Admin data to research value of skills V For Higher Education as well as Further Education

Matched data and social mobility

The longitudinal element of LEO allows a detailed study of social mobility

• Recent research shows the importance of further education to more

deprived groups

• FE shown to act as a important gateway for deprived learners into higher

education

• We need more analysis on the social mobility gains of FE as opposed to

expanding HE

• Also considering how individuals admin records can be linked to parents

Source: Biddy, Cerqua, Gould, Thompson, Urwin (2015)

What data can enabled

• LEO: more data, comprehensive coverage

• Investment POV for individuals and policy makers

• Credible information on courses

• Personalised interventions

• Deep understanding of the system and outcomes

Need a holistic view of education

• Need to understand educational pathways • Using segmentation as a framework to target surveys • Data science and behavioural insights

Apprenticeship scorecard (in development)

Q&A

Publications using the matched data

• Estimation of the labour market returns to

qualifications gained in English Further Education

Franz Buscha, Augusto Cerqua, and Peter Urwin

(December 2014)

• Estimating the labour market returns from

qualifications gained in English Further Education

using the Individualised Learner Record (ILR) Franz

Buscha and Peter Urwin (2013)

• A disaggregated analysis of the long run impact

of vocational qualifications London Economics

(2013)

• Further education for benefit claimants: July

2015

• Adult further education: outcome based

success measures - experimental data 2010 to

2013 September 2015

• Graduate outcomes: longitudinal education

outcomes (LEO) data August 2016

• Improvements to destinations of key stage 5

students: 2014 August 2016