Using data to support progression – the view from HEFCE 10 February 2011 Alison Brunt

Overview: HEFCEs use of data To support policy formation and implementation To evaluate policy outcomes To inform our funding model To inform our knowledge of the sector

Using data to support our widening participation policies Evidence base used to inform our widening access funding methodology Use of data to evaluate and measure success in WP policies Provides us with evidence that our investment in WP is making a difference to under-represented groups

Using existing datasets/sources Types of data and uses HESA, ILR, UCAS, NPD Labour Force Survey Advantages Low burden on institutions Consistent data collection across the sector UK wide Established data source Ability to link datasets Ability to identify progression Disadvantages Limited depth of information

DegreeNeither award nor transferOther awardTransfer Number of starters Projected (%) Bench- mark (%) Projected (%) Bench- mark (%) Standard deviation (%)+/- Projected (%) Bench- mark (%) Projected (%) Bench- mark (%) Total UK Total England Institution A Institution B Institution C Table T5 - Projected learning outcomes: full-time students starting first degree courses 2007/08 Total populationEmployment indicator (including further study) Context statistics of respondents Eligible population Response rate (%) Base population Number employed or studying Indicator (%) Bench- mark (%) Standard deviation (%)+/- Not available for work (%) Refused to take part in survey (%) Total UK Total England Institution A Institution B Institution C Table E1a - Employment indicator: leavers obtaining first degrees from full-time courses 2008/09

Creating new data collections Types of data and uses Aimhigher summer schools Aimhigher Associates Advantages Develop a data specification with the sector which is specific to our needs Evaluate success of specific programmes Disadvantages Guidance needed Time consuming

HE acceptance rates by summer school participation and disadvantaged income background

The middle ground Used existing datasets but with an additional element of collection Advantages National picture of how ASNs are utilised by LLNs Track students progression in the future Disadvantages Only one part of a much wider possible evidence base Identification of LLN learners not always straight-forward Lifelong learning networks – HESA data collection

LLN flagged learners: Full-time, first degree entrants who continue or qualify at the same HEI

Full-time, first degree entrants who continue or qualify at the same HEI

Questions? Alison Brunt Analytical Services Group