EPC subject level TEF consultation: key elements

The subject level TEF could potentially retain the existing key elements of the provider level framework (including the 10 TEF criteria, the same suite of metrics, benchmarking, submissions, an independent panel assessment process and the rating syst

Further information can be found on Pp.13-16 and 17-24 of the consultation document. Based on your experiences on the ground, the EPC would like to offer member university scenarios to demonstrate the impact and/or unintended consequences. Please provide any evidence or supporting information you have in relation to the following statements.
 

The learning environment continuation metric counts full-time students between their first and second year of study regardless of subject or provider. We are unclear about how this metric would capture students switching courses in Engineering, which tends towards student switching: not least because many universities run engineering programmes with commonality in the early years of study so that students can select or switch specialisms in subsequent years of study.

 

Student employment outcomes are largely related to students’ choice of course (Engineering performs well in DLHE), their level of prior attainment, and wider economic factors: providers in low employment regions are always going to score behind regions such as London regardless of teacher quality or student experience, particularly in industry.

 

Engineering currently performs well in LEO data, a reflection of the higher salaries engineering graduates can expect to command. However, given the longitudinal nature of LEO data (and the proposed five- or six-year gap between TEF gradings) possible prospective students will be looking at data that relates to a course that was taught more than a decade previously. This is inherently problematic for engineering, where the pace of change is driven by both technological and industrial revolution, meaning a decade is likely to involve a complete change in industry, labour market and subject currency.

 

LEO data, as regards engineering students, is also likely to have significant gaps. In particular, UK graduates who secure work abroad will not be included which is likely to have a pronounced impact on engineering which has a particularly mobile workforce. This is true in both industry and academia and across all skill levels. Engineering companies tend to recruit from a global talent pool; UK engineers are in high demand internationally and can readily secure employment in other countries.

 

Benchmarking does not fully take into account geographical patterns of economic deprivation and social disadvantage. A further layer of complexity is added when the engineering (and the many courses that encompasses) is placed alongside computing courses and technology courses in its CAH2 aggregation and the different intakes on those courses.

 

Within the current benchmarking methodology, the male-dominated makeup of HE engineering courses effectively means those providers that do succeed in attracting more women in to study engineering will not be made sufficiently visible and will be disproportionately disadvantaged under LEO data (owing to differences in average male and female earnings).

 

Providers have very different missions and approaches (for example, they may choose to focus and excel on access issues, PhDs, regional or industrial engagement) and that they should be measured against that in a way that requires continuous improvement and genuinely stretching targets rather than allow coasting. These dimensions could form part of a more inclusive framework that would explicitly acknowledge the very different focuses and strengths of higher education providers rather than trying to force them all into one fairly rigid model (the TEF).

 

Any other comments on key TEF elements within subject level TEF?