Campus Head and Programme Lead, Imran Mir, explores who defines “quality” in higher education and why current approaches may be narrowing what truly counts.
Quality sits at the heart of the debate in UK higher education. It shapes regulation, influences institutional behaviour and increasingly underpins public legitimacy. Yet despite the many frameworks and indicators used there remains a lack of agreement what quality is and who defines it.
While competence-based quality assurance systems are often defended on the grounds that they demonstrate whether a sufficient proportion of students meet required standards, reliance on averages and proportional thresholds can obscure forms of variation that are essential to genuinely high-performing educational organisations. In complex learning environments, averages are a weak proxy for quality.
Much of the current critique of quality assurance focuses on the limitations of retrospective indicators such as continuation data, graduate outcomes and student satisfaction. These measures are widely understood to provide lagging signals about teaching and learning. The deeper issue is the assumption embedded in the Office For Students’ regulatory framework that aggregate indicators can stand in for educational quality itself.
Quality as a constructed judgement
Quality appears objective but is a constructed judgement. Decisions about what counts as evidence and which outcomes matter most often reflect values and priorities. Regulatory frameworks such as UK Quality Code for Higher Education privilege what can be standardised, benchmarked and compared, because these forms of evidence are seen to support consistency and oversight.
The consequence of this is not merely philosophical. When quality is being defined through what can be aggregated, there are certain forms of educational performance that become systematically harder to see. Learning that unfolds unevenly, develops over longer timescales, or can depend on adapting pedagogical judgement which is often flattened when reduced to averages or thresholds.
Higher education does not just operate as a simple production process, but as a complex system, recognised in systems thinking guidance from the Government Office for Science. Teaching and learning are shaped by context, discipline, cohort and institutional mission. Treating quality as a fixed property that can be taken from aggregate indicators can risk overlooking how educational outcomes are actually produced.
Competence, averages and the limits of assurance
A common rejoinder to critiques of metrics is that quality assurance is fundamentally about competence. If a provider can demonstrate that most students meet the required standards, then the system is working as intended. From this perspective, averages and proportional thresholds appear both reasonable and necessary.
Competence does, of course, matter. Any credible quality system must be able to establish that students are achieving defined outcomes. The problem arises when aggregate performance is treated as sufficient evidence of quality rather than as a starting point for judgement. For regulators, this distinction is critical: it determines whether quality assurance functions as a basic threshold check or as a meaningful evaluation of educational capability over time.
Averages can tell us if competence has been achieved, but they tell us very little about how it was achieved, for whom it worked well, and where it systematically struggled. Two institutions may display very similar outcomes while relying on different educational processes, a distinction which has been recognised in assessment theory.
In this sense, a system can meet competence thresholds while remaining educationally brittle. It may be highly sensitive to changes in cohort composition, dependent on exclusionary practices, or poorly equipped to respond to uncertainty. None of these characteristics are readily visible through aggregate data, yet all are central to educational quality.
Cognitive norms and performance
One under-examined weakness of average-based quality frameworks is their reliance on implicit cognitive norms about how learning should occur and how competence should be demonstrated. Higher education systems tend to assume that a particular model of engagement: how students process and express learning.
Psychological and educational research including work published in The End Of Average consistently can challenge the idea of an average learner. Human variation is the norm rather than an exception. When quality frameworks are being built around statistical averages, they tend to misfit most people, because they are being optimised for a hypothetical middle that few actually occupy.
This is not simply a matter of inclusion or values. It can also be seen as a performance issue. Organisations who operate in complex environments can depend on their ability to accommodate change, exercise judgement and adapt to uncertainty. This dynamic is recognised in frameworks such as those developed by Advance HE to enhance student success.
In teaching and assessment, this can lead to quality assurance processes that reward uniformity while undervaluing pedagogical approaches that are responsive, flexible and context-sensitive.
Quality, complexity and judgement
Higher education institutions are now increasingly operating under conditions of complexity. They face diverse student populations, evolving disciplinary practices and shifting external demands. Research which has been undertaken on organisational performance in such environments, including work on complexity from the Government Office for Science, has shown the importance of cognitive diversity and interpretive judgement.
Quality assurance mechanisms, however, often rely on simplified proxies that assume linear relationships between inputs and outcomes. When meeting numerical thresholds substitutes for deeper evaluative judgement, institutions may optimise for metrics rather than for learning.
The issue, then, is not whether regulation or evidence should exist, but how quality is understood within those systems. Treating aggregate performance as a sufficient indicator of quality risks narrowing the space for professional interpretation and weakening institutional resilience.
Reopening the quality conversation
None of this implies abandoning competence-based regulation or evidence-informed oversight. Rather, it suggests the need for greater caution in how aggregate indicators are interpreted and used. Quality cannot be reduced to what is easiest to measure.
Reopening the sector’s conversation about quality means asking harder questions. What kinds of educational capability do current systems reward? Which forms of variation are rendered invisible? How might quality assurance better recognise the adaptive work that underpins learning in diverse and uncertain contexts?
If quality is to remain meaningful, it must be understood not only as an outcome to be verified, but as a set of educational capabilities that cannot be reliably inferred from averages alone. That is a conversation worth extending.
Imran Mir is a higher education leader with extensive experience in teaching, assessment and governance. He is a Senior Fellow (SFHEA), a Fellow of the Society for Education and Training (FSET), a Chartered Manager (CMgr MCMI) and a Fellow of the Royal Society of Arts (FRSA).
Currently serving as Campus Head and Programme Lead at Apex College Leicester, Imran has a strong interest in leadership, inclusion and staff development. His work focuses on embedding inclusive practice across further and higher education, with particular attention to neurodiversity and mental health. As someone who is autistic himself, Imran draws on lived experience to challenge traditional approaches to leadership and advocate for systems that enable both students and staff to thrive. He regularly contributes to sector discussions through articles and blogs.
