While we may be on the fifth iteration of the Teaching Excellence Framework (TEF), with different purposes and plans for execution, one thing has remained pretty consistent throughout every version. That is the proposition coded into the “metrics plus provider submission” model that institutions have a sense of how what happens in classrooms translates into student outcomes.

A quick note for non-England colleagues – we’re using the TEF as the hook for this discussion, but we think the proposition has relevance for any institution looking to work out where to focus resources to deliver strategic quality outcomes. Please do read on!

Higher education quality assurance is grounded in consistency: in processes of internal programme development, in external scrutiny, and in the application of standards. These things matter for assuring and enhancing quality, but for the demonstration of quality via the TEF, they matter less than an institution’s ability to show the evidence of how it knows that the result is a high quality student experience. Describing those processes and the policies they enact won’t count as evidence of quality.

The killer line from the most recent TEF consultation is this:

The submission should focus on the experiences of students and provide a range of evidence used by the provider to understand the quality of their experience. Where a submission describes the provider’s activities, policies or processes, it should show the impact on the student experience and the scale of that impact across the student population.

We had a quick look, with the help of our robot friends, at the types of evidence that showed up in the provider submissions of the 59 institutions who achieved a Gold rating for student experience in the 2023 TEF exercise. Internal student survey data, including module evaluations, pulse, induction and service satisfaction surveys, was used in 86 per cent of all the provider submissions we analysed.

Stripping out reference to the TEF metrics and NSS itself, student survey data comes second only to service engagement and usage metrics. Other impact-focused evidence, for those interested, included external examiner reports, external awards and reviews, internal evaluation data, course review, accreditation review, and internal audit data, student-led teaching awards data, employer insight, and graduate and alumni insight.

While we might only have proved that internal student survey data is one of the most readily available datasets, we’d still argue that it’s all but impossible to demonstrate impact on and across the student population without systematic survey data. Now is the time to find out whether your student survey system is delivering the insight you’re going to need to make that case.

A key difference between that exercise and the forthcoming one, is that institutions will be assessed on a rolling cycle, with those most at risk prioritised, giving the rest a bit more time to prepare. While previous exercises basically asked institutions to pull together whatever was available to build a retrospective case, at pace, this one involves a bit of actual lead time. There will still be a strong backwards looking element – we’re talking about the laggiest of lag data in the form of NSS results from the prior three years – but there’s much more opportunity to interrogate that data, find its weaker points, and set an evidenced trajectory towards improvement.

The holy grail of lead data

In our work together, Wonkhe and evasys have been exploring the ways that student feedback can most helpfully be deployed in the service of strategic student success objectives. In investigating the idea of strategic alignment between feedback and success, in consultation with sector colleagues, we quickly realised that before we could talk about vertical strategic alignment, we had to start by describing the way that elements of a student survey feedback system support each other to generate meaningful insight.

Students and staff need to understand and trust the system to engage with it at all, and in order for that to happen there has to be governance of the system as a whole, with named individuals tasked with designing and implementing it. This insight led to the creation of our Survey feedback for student success framework.

The next step, with TEF looming, is focusing in on the second “rung” of our framework: the strategic alignment element, which asks institutions to test and validate three statements:

• We are confident our survey practice aligns with our student success objectives and enhancement work, and we use evidence to demonstrate it.
• Staff use survey data to systematically enhance modules, programmes and experience, and are supported to evidence how, in partnership with students.
• Students grasp the link between their own efforts, experiences and environment, and can offer constructive comment to improve it.

Unpacking these ideas, what we’re proposing is that if you look at the institutional performance indicators around student success, the teaching and learning strategies, policies and curriculum frameworks, the disciplinary approaches and pedagogies and the stuff students get asked to feed back about through module or programme questionnaires or in pulse surveys, you should be able to trace some degree of coherence of intent.

In practice, this can sometimes look like relentlessly surveying students with the same NSS questions you hope will be answered in the affirmative on graduation. But to do that skips the middle bit – the bit where the abstract ideas of quality student experience that NSS attempts to capture are rendered real and meaningful in authentic ways for the institution as a whole and, cascading down from there, in different programmes, disciplines, and modules, or at different points in the student journey. The intention should be that staff and students have a shared working sense of what quality looks like in their immediate context and are in a position to check whether it’s happening and what needs to be done to make it better.

There’s lots of ink spilled about the legitimacy of NSS as a quality metric, and we’re not proposing to rehearse it here. In reality, your institution will have its own view about whether NSS should show up in performance indicators. It’s clear that TEF intensifies the rationale to do so. If that’s the decision, then arguably the pressure is even greater to build a quality management system that avoids giving the impression that the goal is to game the metric, and that makes sense for the institutional context.

There’s obviously an inherent tension between the diversity of how courses are taught in practice, and building a single systemic institutional point of truth on quality monitoring. As we discussed when we launched the project that led to the creation of the student feedback framework, student feedback and evaluation of modules can legitimately serve many different purposes, but not all of them can comfortably coexist. Student feedback shouldn’t feel like surveillance. It can be complicated, and sometimes a bit fraught.

Despite that, we think that if the institution has a goal around the quality of student experience, it makes a lot of sense to look for “lead indicator” moments, where you can check how that goal is manifesting consistently – another important TEF word – for diverse students in their modules and programmes.

What it looks like in practice

Giving a worked example feels a bit daring, but let’s say for the sake of argument there’s a strong institutional ethos around preparing graduates for the workplace, and a strong emphasis on applied knowledge in your learning and teaching strategy. You might, quite reasonably, adopt the NSS question about “opportunities to apply what you’ve learned” as a key performance indicator and decide that performance above benchmark is the goal.

Cascading that down to modules or programmes, you’ll quickly find that the idea of applied knowledge means something very different in Year One and Year Three, or in the allied health disciplines versus the social sciences. Thinking carefully about what you’re trying to achieve and the nature of the insight students can offer at each stage in relation to that should feel like a useful and productive conversation, should offer a sense of what you might want feedback on for each stage. You might also usefully develop a view on the contexts that only require a quick mid-module pulse survey sense check – are students aware of it happening, yes or no? and what do we do right away if it turns out that lots are not? – and those that would reward a more nuanced exploration of the concept, such as what’s working for students, how it’s making them feel, what skills it’s developing, such as might be appropriate for evaluation of a module or capstone experience in which application of knowledge is a core learning outcome.

Or, let’s take a different way in. Assessment and feedback has been a long standing priority area for your institution, and you’ve done some good work exploring the issues and running projects, and introduced some new questions into module evaluation that aims to capture issues at that level. But you want to test the predictive power of those questions, singly or in combination, within different subject areas, to NSS outcomes. To do that you need to bring the different datasets together in a dashboard and start playing. There will never be a perfect correlation, but with a bit of work, triangulated with wider staff and student insight, you might be able to identify something that seems to matter more than something else – and that’s your lead indicator.

One of those approaches might appeal more than the other – that’s fine. Either version could plausibly show up as evidence in TEF. But, taking TEF out of scope entirely, we’d argue that all institutions will be thinking carefully about where to focus their enhancement resources, whether that’s about bringing a particular area up to scratch or about doubling down on the thing you intend your students should remember most vividly about their experience with you. We’d love you to bring us with you on that journey.

In 2026–27 Wonkhe and evasys are convening a group of institutions in the form of an “action learning set” to explore real-world ways of finding lead indicators for quality enhancement. You can register your interest in getting involved here, with no commitment at this stage. 

This article is published as part of a partnership with evasys.