Mo Zandi explains a proven model to manage academic workloads
A MODEL to help allocate academic workloads in a simple, easy-to-implement and transparent manner has been developed by the UK’s University of Sheffield’s Department of Chemical and Biological Engineering. After five years of successful trials, the administrative tool – which can help academics better understand their contribution to the university and plan their time more effectively – is proven and ready to be implemented elsewhere.
Ever since the 80s and the emergence in the UK of the so-called “New Public Management” under Margaret Thatcher’s Government, the public sector, including universities, has increasingly been subject to the forces of managerialism, marketisation and privatisation. This has brought an increased emphasis on performance measurement to ensure value of money for university customers – as epitomised by the observation in the Jarratt Report, a 1985 inquiry into British higher education, that the “time of academic staff is the primary resource of a university, and it needs to be managed and accounted for with appropriate care and skill.” This sentiment has only been strengthened in the wake of both the Browne Review of 2010 – which saw students taking on much more of the costs of their education themselves – and the dissolution of the Higher Education Funding Council for England in 2018.
My interest in the administration of academic departments began six years ago, when I was thrown into the deep end and put in charge of teaching and learning in the Chemical and Biological Engineering department at Sheffield. One of the most challenging parts of this role is allocating teaching across the academics. Having spent a couple of years working in industry – where you are allocated a certain number of working hours to a project and complete a timesheet each week so managers can have a good view of how their reports are distributing their time – it struck me as odd that there was no real equivalent system in place here.
Of course, many academics might find it jarring to be asked to complete a weekly timesheet, given the degree of autonomy customarily associated with the profession. At the same time, however, the business of the university does require certain activities to be completed. Without some form of framework for how much time each task really requires, it is difficult to navigate any disagreements over whether workloads are manageable, whether responsibilities are being fairly divided among the staff, or if time is being split reasonably between competing tasks like research and teaching. For example, in our department, there was previously no explicit link between one’s allocated teaching duties and the number of students one was teaching. It was all too vague and uncertain – creating problems for the administrative leaders and individual academics alike.
Looking into the issue and trying to find examples of best practice elsewhere, I found plenty of principles published to guide workload allocations – such as, for example, that full-time academics should work a given number of hours per year and that tasks should be assigned fairly, equitably, and transparently – but little in the way of how to realise these goals into a practical system. When I looked at how other engineering departments operated, I found that they all had their own different approaches: some used a rule of thumb to roughly dole out tasks, while others had created spreadsheets so complicated that they themselves had no idea any more how the numbers were calculated. To avoid falling into the same trap, my colleague Jim Litster and I set about interviewing everyone in our department – all 35+ of us – to find out how long they felt they spent on various key activities and what kind of affordances they would like to see built into a general, practical, and mathematically-defined work allocation model going forward. Only one member of staff objected to the principle of using a work allocation model.
From this, we built up a mathematical equation for the total time spent on departmental business, expressed as a percentage of a full-time equivalent (FTE) workload (WL). Then we took it back to the academics so they could share what they thought of it. I really think that the key to the success of the model lies in how everyone has been able to offer their individual feedback as we have refined the underlying formula. Everyone has really had a chance to buy in to the system – ensuring that the model is transparent and fosters confidence in those that use it – and this has really left us in a much better position as a department.
At the heart of the model are the activities that we agreed were also at the heart of any educational institution: teaching, research, and admin. The first part of the model allocates time needed for teaching tasks. It takes account the number of students taught (xi) registered for each given course or module (i) and the credits such are worth (nc,i), alongside the number of students doing their final-year projects that the academic is supervising (x2). By factoring in the number of students, we acknowledge that teaching a module with 200 enrolments is, obviously, quite a different undertaking than running one with all of 20 students. Constant A is a fixed value for the time taken to prepare and deliver a module and for module leaders to coordinate and provide quality assurance, while constant B represents the time needed for assessment and feedback – including marking assignments and examinations, compiling and submitting examination results, and providing feedback for any module.
The next constant, C, is included to cover the time our academics spend supervising what we call third-year design teams – which are specific to the chemical engineering programmes. While the design teams may be unique to our curriculum, it shows how easily the model can be adapted to meet a specific department’s requirements. So, for example, if the model was being used in a biology or geology department, similar affordances could be built in to cover special activities like taking students on field trips. The last teaching constant, D, accounts for the time taken to supervise and assess students like those on MEng and MSc research projects, which are the equivalent of a final-year undergraduate project in many other programmes.
For admin tasks, the model considers both formal administrative roles (F) but also a fixed constant (G – set at 5% FTE for all academics) which accounts for citizenship activities like examination meetings, attending open days, staff meetings, etc. Finally, the research part of the formula accounts for the number of research hours (y) allocated to a given research contract (h), the number of PhD students being supervised (x3) and time spend on research business development (J).
To keep things straightforward, we have confined the model to significant tasks only – those that account for at least 5% of an FTE workload. In our department, teaching and research academics are allocated a combined teaching and administration load of between 30–70% full-time equivalent depending on their research load (and usually in the range of 40–50%).
Table 1 shows three hypothetical work allocations generated by the model for teaching and research staff – that of a typical lecturer (72% FTE), senior lecturer (96% FTE), and professor (101.5% FTE). As you can see, the model has predicted different workloads based on the staff member’s profile and activities. The lecturer’s load is lower than the other two, reflecting their early-career stage as they develop their research and teaching activities. The senior lecturer and professor, meanwhile, have higher loads as they are more advanced in their career, especially with regards to research activities. The main thing here is that the model is transparent and allows both managers and staff to understand how time is to be split among different activities. It also allows staff to have a meaningful conversation with their line managers if their circumstances change or they require support – such as if they are awarded a major research grant, or need to reduce their hours due to personal circumstances.
It is important to stress that our work allocation model provides an indication of the relative effort that should be involved in each given academic’s activities – but at the same time makes no assumptions regarding the effectiveness of any individual in any task. To borrow a phrase from the great British statistician George Box, “all models are wrong, but some are useful” – a maxim that well applies to our work allocation tool.
Within the department, the model has been overwhelmingly well received by both academics and administrative leaders alike. We have had no negative feedback in as far as how the model accounts for all the different kinds of activities, or around how easy it is to understand. Early responses from staff included statements like “it is sensible”, “having transparent data helps” and that they particularly “liked the inclusion of [research grant] hours” in the model.
One of our staff members – Siddharth Patwardhan, Professor of Sustainable Chemistry and Materials – had this to say about the work allocation system, after living with it for five years: “This model has allowed the allocation of teaching and administrative tasks (while not measuring outcomes) in a fair and transparent fashion, based on what academics do – rather than seniority, history, negotiating skills, etc. It has improved transparency and efficiency in distributing departmental duties.”
Laura Maltman, Departmental Administration Manager, with responsibility for HR and staff wellbeing, said: “This model plays a valuable role in ensuring a fair, consistent and transparent approach to what is otherwise a very complex area. Having worked in a number of academic departments, the work allocation model developed in here is exemplary, giving a comprehensive overview of work distribution in an open, transparent and easily-accessible format. We all have increasing demands on our time and the irony of spending lots of ‘time’ on workload allocation itself isn’t lost on me, but the model ensures an efficient use of everyone’s time across the board.”
What has also really given us confidence has been comparing the work allocations produced by our model with the results of the university’s most recent triennial Time Allocation Survey of all staff, which last ran from May 2019–20. We found that there was a very good correlation between our model and the times reported by our academics.
In fact, the only issues that have arisen have been from a couple of academics who felt that the specific variables assigned to them didn’t quite work for their circumstances – for example, one whose module involved the marking of an especially long report that requires more time than usual to grade. Such issues are easy to overcome by acknowledging the difference in teaching styles and changing the relevant variables on a case-by-case basis.
At the same time, however, the work allocation model has also helped prompt some academics to take a second look at the length of time they are spending on certain tasks, question whether this is good for them, the students and the department as a whole – and explore whether there might be more efficient ways to achieve the same ends. For example, while we don’t share everyone’s individual data, we can provide data on how long it takes the average academic in our department to grade a report of a certain length or weighting and thereby help our staff identify when their marking might be taking too long or be too involved. This can help people plan their time more carefully.
In a similar fashion, we can look at the average teaching load and the average research load and identify when staff have taken on too many responsibilities, and might need to lighten their workload accordingly, or get additional support. The typical range workloads in our department run from 70–120% FTE. For example, while I’ve learnt that my workload is well above the department’s average, and one of the highest in the department, this has allowed me to have a constructive conversation with my line manager to receive support and plan to reduce my workload in the future.
The other thing I will note is that it’s best not to just consider the data from the model annually, but one should look at the average from the last three years. This helps to account for the way that academics often have busier and quieter years. So, while one might have a workload of 105% FTE one year, this might be balanced out by one at 95% FTE the next year and still lead to an average of around 100% FTE.
The flexibility of the model to adapt to unexpected circumstances has already been demonstrated during the Covid-19 pandemic which, naturally, forced us to switch to online teaching. Recognising that modules would need to be significantly retooled to be delivered virtually, we were able to take the allowance in the model for time to significantly improve or change a module and use that to give all the academics an extra 10% FTE – and then see how that affected the overall workload and adjust other areas to compensate. Many academics were initially worried that this extra work wouldn’t be accounted for, but the transparency of our model helped allay their concerns.
The impact of the pandemic is fully expected to lead to a decline in university incomes, which in turn will trigger further cuts and increased workloads – applying yet more pressure on university administrations to manage their limited resources more efficiently
It is also worth noting that the impact of the pandemic is fully expected to lead to a decline in university incomes, which in turn will trigger further cuts and increased workloads – applying yet more pressure on university administrations to manage their limited resources more efficiently. This will make practical work allocation systems like our model even more valuable.
The data collected by the model also offers a lot of potential for useful demographic analysis. For example, one can use it to explore the different workload distributions between women and men in a department. That’s an important weighting when looking to empower women in academia – and this is something we have been doing in our department since the introduction of the model. This allows women in significant leadership roles to have a constructive discussion with their line manager to ensure that their workload is equitable with their peers – and to receive support or a reduction in other activities such as teaching in order to maintain a healthy workload.
There may be some potential to further refine the variables in our model, but from a management perspective in our department we have essentially reached the business-as-usual phase of its application. In fact, we have integrated the model into our institutional intranet – setting up a web-based app through which our staff can directly access their work allocation figures online.
We’re keen now to share the model with others as an example of a useful tool that could be tweaked to help administrators and academics in other departments and institutions. We are yet to promote the model at the university level, so there are no plans to roll it out across Sheffield (at least not yet). That said, however, we have shared the model with a couple of colleagues outside of our department at Sheffield – as well as in chemical engineering departments at other institutions – and the feedback so far has been overwhelmingly positive. They were all basically saying: “Can we have a copy of it?”.
At some point, I have plans to write a handbook on management in higher education; one aimed not at vice chancellors and the like, as is typical, but at a “shop floor” management level, something for academics like my younger self. Our work allocation model – and how to adapt it for a given setting – will certainly feature in that.
Until then, well, one of the beauties of the model is that it is not very complicated. All you need do is to define your own values. Anyone with a school-level understanding of mathematics can easily customise it to suit their needs.
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