We won’t reduce inequalities in post-16 progression until we make ‘lower attainers’ more visible

By Ruth Lupton, Stephanie Thomson, Lorna Unwin and Sanne Velthuis

Inequalities in post-16 progression

The continued use of GCSEs as a blunt instrument for dividing pre-and post-16 education is one of the main causes of inequality in the English system, with impacts extending well into adulthood. The system asks the least confident, least academically successful young people, often (but not always) facing greater social and economic disadvantages, to make the most complex, life-shaping choices at the youngest age.  Contemporaries with high academic attainment can progress more straightforwardly in a simpler, better understood, and historically better-funded system, often postponing decisions about occupational directions until age 18, 19 or later.

In our new research, funded by the Nuffield Foundation, we investigated the post-16 trajectories of young people who we described as ‘lower attainers’ – the 40% of each GCSE cohort who annually do not achieve a grade 4 (formerly C) in both English and maths.  We presented our findings at a recent CEPEO webinar.

Our research employed a mixed-methods approach combining analysis of data from the National Pupil Database (NPD) and Individualised Learner Record (ILR), collection and analysis of local data about course and apprenticeship opportunities and entry requirements, and interviews and focus groups.

It shows how, in making the transition to the post-16 phase and attempting to progress beyond GCSEs, ‘lower attainers’ face multiple barriers including: inconsistent careers information and guidance; restrictive entry requirements that are often based on English and maths GCSEs (even when it is not clear why specific grades are needed); considerable local variation in accessible provision; and the low availability and poor visibility of apprenticeships. Apprenticeships are not the accessible pathway for ‘lower attainers’ that many people imagine, with only 5.8% moving into an apprenticeship at 16 in the 2015 cohort, for example.

It also shows that many young people start their post-16 phase on courses below the levels of learning they have already achieved and that learners with similar attainment at 16 enter the post-16 phase at different levels in different places, partly due to local differences in the mix of provision and institutional practices. This has potential repercussions for the achievement of Level 2 and Level 3 qualifications between 16 and 18/19.

Making the problems and solutions more visible

All this points to a complex and locally variable picture that needs to be better understood.  But achieving clarity and understanding is very difficult due to the way attainment is measured and administrative data is collected, organised and made accessible.

Published statistics do not make the achievements and trajectories of lower attaining young people very visible, probably because much of the policy focus to date has been on raising KS4 attainment at the standard benchmarks. Coverage of lower-level qualifications (and of spatial variations) still lags behind.

And beyond the published statistics, there are major problems with the capacity for detailed analysis of the underlying data.

One issue is the data itself. Currently, we have two different large-scale administrative datasets for the post-16 phase – the NPD and ILR – with different definitions, variables and standards of documentation, and including different learners.  Getting access to these involves a lengthy and difficult application procedure, and working with the data to summarise what learners are doing and achieving is a painstaking process. Looking at academic routes is easier than tracking routes through vocational courses and apprenticeships because matching NPD (Key Stage 4) to NPD (Key Stage 5) is easier than matching NPD to ILR.  It is easier to look at outcomes than it is to understand progress and what learners are actually doing.  So analysis often focuses on qualifications achieved as the data is collected in this way.  We tried a different approach.  We developed a measure of a learner’s ‘main level of learning’ – the level that they were spending most of their guided learning hours on – and thus were able to illuminate progression (or not) from levels already achieved.  If the data sources were easier to access and use, much more could be done to analyse and explain course changes and progression between 16 and 19 and to understand what constitutes success and progress.

At a local level, basic information on the system in terms of the nature of provision at any given time as well as associated entry requirements is not routinely collected. To shed light on these issues, we had to collect and aggregate this information from provider and national agency websites, a labour-intensive task. The lack of available data leaves policy-makers unsighted as to what is on offer, who is missing out, and which gaps need to be plugged.

The other issue is analytic capacity.  Even if there were better data, there is a paucity of academics with interests and expertise in further education and training compared with the numbers working on school and higher education research. And we need more research teams who can combine quantitative and qualitative methods to investigate the relationship between the pre and post-16 phases. Changing this now will require not just funding for projects and centres but investment in early-career scholarship, addressing status issues and links to teaching. And there are insufficient links between people who have the skills for data analysis and practitioners who understand how the system works on the ground. Cuts to local authority funding have further diminished local capacity and intelligence.

Thus, if the characteristics and trajectories of lower attainers at GCSE are to be better understood on an ongoing basis, three substantial changes will need to be made:

• Routine reporting of sub-benchmark achievement in more detail, and at relevant subnational scales.
• Improvement in data infrastructure and access.
• Increase in research and analysis capacity, both in local government and in universities and research institutes, and better links between them.

These will not be cheap.  But if the government is serious about eroding the long-standing inequalities in post-16 progression, it simply must invest in making the situation more visible.

The research reported here was funded by the Nuffield Foundation, but the views expressed are those of the authors and not necessarily the Foundation. Visit www.nuffieldfoundation.org