There are many different approaches to workload modelling and therefore there is a great variability in what they look like and how they are used. However, at the same time there are some underlying commonalities. The vast majority of them use categories to identify academic work. Most models split academic work into three categories: research, teaching and administration. These categories then have percentages of time, or actual hours attached to them. For a research and teaching contract this might lead to 40% research: 40% teaching: 20% administration. For a teaching dominant/fellow contract it might look more like 20%:60%:20% or even 10%:70%:20%. These percentage splits can then be easily translated into numbers of hours. Whilst some this as an assault on autonomy, for others it is a basis for ensuring fairness.
Whilst at first sight this form of modelling might feel attractive and might clarify the expected amount of work by academics, there are several aspects of WAMs which can become problematic. Hull (2006) stresses that WAMs are developed in different ways. For example, what is the origin of the WAM? Has it been developed and is it run by the HR department, by a faculty or a department? In otherwords how close is the modelling to the reality of the work done by academics. This is essential as the distance between those creating and managing a system and those who are doing the work can cause many misunderstandings or misinterpretations. Also, what is the level of granularity or detail of the model? Is it based on 1/10ths of an hour, on a points-based system or on the number of students taught? These will give very different outcomes, and ones which might feel very iniquitous. For example, one lecturer might get much more ‘time’ in a student numbers-based system for two hours lecturing than someone involved in a two hour seminar. The amount of time to prepare might be just as great, indeed, for a seminar it may take longer as it might not be possible to dust down the PowerPoint from the previous year, and yet would have to teach many more hours than their colleague giving the lecture to reach the same tariff. To these two issues which Hull identifies, I would add a third, and possibly the perceptually most damaging – that WAMs are merely a cost exercise dressed up as having an interest in time. If WAMs are actually a resource allocation exercise predicated on a limited financial bottom line, this should be made clear; to be told that a 5,000 word assignment should only take 20 minutes to mark (and that anyone who can’t is simply not trying hard enough) when actually that is the only way of making the money fit is being disingenuous and is undermining the validity of the process.
So before considering some of the inherent challenges and inadequacies of WAMs (the focus of a future post), what are some of the basic processes and characteristics which need to be put in place to maximise the chance that the outcomes of modelling will be in some way accepted by academics? Kenny and Fluck (2014) begin by outlining the main problems with WAMs. The challenges they see are:
- How do you identify and quantify a large range of activities with credible estimates?
- How do you account for non-routine activities? For example, teaching is a process based on experience and expertise. What takes an experienced lecturer an hour to put together, a novice may need three or four times that. It is not a single, routine process.
- Some academics believe WAMs curtail both flexibility and autonomy.
Therefore, if there is any chance of a WAM getting anywhere close to being credible (let alone accurate), Houston et al (2006) argue for four characteristics of a model which might help make it better:
- There needs to be the incorporation of department specific elements to the model. Work differs between disciplines, and therefore a single institutional model won’t work, and most importantly will be seen as inequitable by some, the very reasons models have been seen as necessary in the first place.
- There needs to be consultation and collaboration with those the model will cover. To treat it as merely an accountancy exercise will again be seen as unethical and may lack the credibility and equity needed.
- Transparency in application. Again, if a model is applied mechanically and individuals merely told what their personal outcome is, there will be feelings of loss of professionalism, of equity and the related opacity is bound to be seen as unfair.
- Staff confidence that tariffs are realistic.
Without these as a bare minimum for beginning to consider the form of a model, there will be charges like that put forward by Papadapoulos (2017),
‘Workload models are a prime example of simulacra: they are not grounded in an empirically derived approximation of the time activities take: in other words, they do not proceed from the ‘real’.’ (514)
In the next post, I’ll consider a host of other potential barriers which need to be thought about in developing WAMs, and whether we can actually produce any modelling approach which will have credibility, equity, and which art the same time ensures the flexibility and autonomy crucial to academic work.
Houston, D., Meyer, L.H., & Paewai, S. (2006) ‘Academic staff workloads and job satisfaction: Expectations and values in academe’ Journal of Higher Education Policy and Management 28:1, 17–30.
Hull, R. (2006) ‘Workload allocation models and “collegiality” in academic departments’ Journal of Organizational Change Management 19:1, 38-53.
Kenny, J.D.J. & Fluck, A.E. (2014) ‘The effectiveness of academic workload models in an institution: a staff perspective’ Journal of Higher Education Policy and Management 36:6, 585–602.
Papadopoulos, A. (2017) ‘The mismeasure of academic labour’ Higher Education Research & Development 36:3, 511-525.
Wolf, A. (2010) ‘Orientations to Academic Workloads at Department Level’ Educational Management Administration & Leadership 38:2, 246–262.