Most universities now use some form of workload model. At first glance, this seems entirely sensible. Academic work needs to be allocated, balanced and monitored. Teaching has to be covered, research time needs to be allowed for, supervision has to be factored in and supervision, administration and leadership responsibilities must all be distributed across the institution.
Without some form of workload allocation, universities would struggle to operate effectively. Yet despite this, workload models remain one of the most debated and contested aspects of university life.
Why? Because academic work is extraordinarily difficult to measure consistently and fairly.
The problem starts with measurement
Some activities are relatively easy to quantify. A university can estimate:
- Lecture hours
- Tutorial delivery
- Student numbers
- Marking loads
- Committee memberships
- Doctoral supervision
- Grant applications
- Publication outputs
These activities can be converted into hours, percentages or points. The challenge begins when universities try to recognise the enormous variation that exists underneath those numbers.
Is a lecture to 20 students equivalent to one delivered to 300? Should preparing a brand-new module count the same as repeating the same lectures that have delivered for several years? How much time should be allocated for marking complex dissertations compared to multiple-choice assessments? Is supervising a struggling doctoral student equivalent to supervising one who is highly independent? And these questions only scratch the surface.
Even apparently simple activities quickly become difficult to standardise.
Different disciplines, different realities
Research creates another layer of complexity because different disciplines operate in very different ways. In some fields, research may involve laboratories, large teams and major grant funding. In others, it may primarily involve individual scholarship and long-term theoretical work.
Publication patterns also vary significantly. Some disciplines publish multiple shorter papers each year, while others may spend several years producing a single monograph or major journal article.
Universities often attempt to create institution-wide workload systems that apply consistently across all disciplines. However, academic work is not naturally uniform. This creates an ongoing tension between consistency and flexibility. Too much standardisation can feel unrealistic, while too much flexibility can feel unfair.
The challenge of invisible work
there is the issue of invisible work.
Universities depend heavily on activities that are often difficult to quantify properly, including mentoring junior colleagues, supporting students in difficulty, building industry relationships, reviewing papers and grant applications, contributing to academic culture, giving external talks and representing the university externally in other events (student recruitment, external examining etc.).
These activities can consume significant amounts of time, yet they may not fit neatly into formal workload categories. Even when institutions try to recognise such work, assigning an agreed “value” to it becomes highly subjective.
How many hours should mentoring a colleague count for? How should universities account for pastoral care? What is the workload value of preventing a problem before it escalates?
These are not easy questions and there are no easy answers.
Fairness depends on who you ask
One of the biggest difficulties is that fairness itself is subjective. Two academics may look at the same workload allocation and reach completely different conclusions.
One person may focus on contact hours. Another may focus on research expectations. Someone else may emphasise administrative complexity, while another believes that the demands of student support are underestimated.
Academic work is also uneven across the year. Some roles involve intense pressure during admissions, accreditation, examinations or grant deadlines, followed by quieter periods later. Others involve continuous responsibilities that are less visible but persist throughout the entire year.
Capturing all of this within a single model is exceptionally difficult.
More data does not always mean more agreement
Technology has helped universities build increasingly sophisticated workload systems. Many institutions now use detailed software platforms capable of tracking teaching, supervision, administration and research activity with considerable precision.
However, more sophisticated systems do not necessarily eliminate disagreement. In some cases, they may even increase it because the more detailed a workload model becomes, the more questions emerge.
Has every activity been included? Are the weightings correct? Is the allocation realistic? Are disciplinary differences recognised properly? Is quality being considered, or only quantity? Is the system transparent?
At some point, universities often discover that workload modelling is not simply a technical exercise. It is also a human one.
Why this is ultimately a leadership issue
There is also an important leadership challenge underneath all of this. Universities are communities of highly educated professionals with very different perspectives on what academic work involves. That means workload discussions are rarely only about hours.
They are often about recognition, trust, transparency, institutional priorities and professional identity.
When academics feel that important parts of their work are overlooked, workload models can quickly become symbolic of wider institutional frustrations. Conversely, when staff believe the system is broadly fair and transparent, workload models can help create trust and stability even if nobody believes the model is perfect.
That may ultimately be the key point.
Perhaps the goal is not to create a workload model that everyone agrees is perfectly fair. That may be impossible. Perhaps the more realistic goal is to create systems that are transparent, adaptable, evidence-informed, sensitive to disciplinary differences and open to challenge and revision, while recognising that academic work itself is often too complex to be reduced entirely to formulas and spreadsheets.
The search for a fair system
The search for a perfectly fair workload model may never end because universities themselves are complex human institutions built around highly varied forms of intellectual work.
And perhaps that is precisely why workload models continue to generate such strong debate across higher education.
About the author
Graham Kendall is Acting Vice-Chancellor of GlobalNxt University and an Emeritus Professor at University of Nottingham. His work focuses on higher education governance, research strategy, scholarly publishing and institutional leadership. He regularly writes about the systems, incentives and governance structures shaping modern higher education and research.