Advancing academic workload planning: Insights and strategies from Professor Nicki Lee

Universities are grappling with significant challenges in planning and managing academic workloads effectively. These challenges are multifaceted and vary across faculties, disciplines, and institution types. 

La Trobe's Professor Emerita, and previous Deputy Vice-Chancellor Education, Professor Nicki Lee, thinks that it’s ultimately a question of data, recognising that “without quality data, there’s a lack of visibility."  And it’s principally this lack of quality data that the new UniForum Teaching Effort Analytics program is addressing, by providing universities with robust, comparable insights to help inform better decisions around academic workforce planning, teaching delivery and portfolio design.

Siloed systems and guessing games

Academic workload processes tend to be localised, happening at the department level, which can create a disconnection from higher administrative levels. This disconnection results in limited granularity and the inability to triangulate data effectively in order to generate broader workforce insights. Equity issues can also arise from this lack of detailed and robust data. 

Departments and schools often operate in silos, with supervisors and department heads making workload decisions based on incomplete data. This situation leads to uneven distribution of workloads, where some faculties or staff categories may be overburdened while others are underutilised. “Limited information about comparative distribution results in a lot of guesswork," says Professor Lee. 

Moreover, competing priorities such as teaching and research further complicate workload planning. “The conversation takes into account service and research activity," says Lee, “and, depending on the organisation, either teaching or research may take precedence, impacting how workload hours are allocated and balanced.”

New tools and innovative strategies

To address these challenges, several promising tools and methods are emerging. Advanced course management and workload planning systems that offer high-quality data play a crucial role. “There are a multitude of tools that do now actually provide you with good data," Professor Lee says. These systems provide comprehensive visibility across departments, as well as historical data, allowing universities to make better, more informed decisions. 

Even simple yet effective tools, such as Excel spreadsheets, have proven useful, she says, allowing deans to review course and subject numbers, staffing levels, and workload distributions. "The point is to allow deans and others to see where the weight of the work is happening," Professor Lee explains. 

Investing in the right human capabilities is equally important as the systems you use, though. “We need people with expertise in the sector and in data," she says. Universities need analysts who understand both the academic environment and data management in order to develop relevant dashboards and tools that align with institutional goals and provide actionable insights.

Data leads to better decisions – but it can also generate support for change

Organisations must be prepared to invest in systems and visible data analysis that supports evidence-based decision making. "You need to be able to make conscious decisions about how to manage a portfolio in ways that make the most of the discipline range while limiting the burden on academic staff and reducing casualisation," Professor Lee says. “Otherwise it is impossible to realistically gauge where there is unnecessary or skewed workload, or issues emerging around portfolio or EFTSL growth." 

Data-driven decision-making can be useful for planning new courses as well, giving a clearer view of the opportunities for maximising portfolio and evaluating costs. 

Of course, the words hidebound and stubborn get thrown around a lot in conversations about organisational transformation and universities. Professor Lee says that clear and incontrovertible data has a critical role to play here as well, often proving essential in overcoming initial resistance from stakeholders. "When you have data that stakeholders can read, understand, recognise and count on," she says, “it’s so much easier to generate engagement and support at various levels of the organisation and make informed workload planning decisions.”

The role of Teaching Effort Analytics

One critical tool in the emerging toolbox, which exemplifies the use of data-driven approaches in academic workload planning, is UniForum Teaching Effort Analytics. This analytics platform integrates detailed data from across departments and faculties, enabling universities to quantify and visualise teaching efforts comprehensively. Beyond that, a consistent and rigorous data-collection methodology enables meaningful cross-university comparison offering universities insight into how their teaching practices and resource allocations differ from peers. By providing robust data and insights into workload distribution, Teaching Effort Analytics helps institutions address the frustrating lack of visibility that Professor Lee highlights. 

With Teaching Effort Analytics, universities can elevate local data discussions to broader administrative levels, ensuring that workload planning is grounded in precise, comparable and actionable information. This comprehensive approach aligns with the other emerging tools and strategies Lee recommends, such as advanced course management systems and detailed analytical tools, to create a more equitable and efficient workload distribution system. 

“Teaching Effort Analytics starts to fill the gaps in the system and then adds value by allowing you to look beyond your own institution and understand how ‘normal’ your practices are,” Professor Lee says.   

“It’s a bit of a Trojan Horse, in some sense, because it does so much more than provide an overview of your own data. You could feasibly do that internally, even if most universities struggle to. But they’ll never be able to do the benchmarking at scale, which is where things get really interesting. That’s when we will start to see robust discussions about the decisions and choices that can be informed by teaching workload data.” 

Strategic steps to success

To advance academic workload planning, universities must take several key steps. Firstly, investing in advanced tools and systems is crucial. "We now have better tools, and this is the time to learn how to use them in the context of a university to get the insights we need," Professor Lee says. 

Secondly, building the right capabilities within the university matters, too. As previously mentioned, training analysts who understand both the academic and data management contexts ensures that the tools and systems are used effectively. 

Thirdly, fostering a culture of collaboration and collective buy-in is important. “It needs to be everybody's data, in a way, and everyone needs to feel that they have a stake in it.” Involving Vice-Chancellors, councils, boards, senior executive teams, and academic leaders in the data and decision-making processes ensures that everyone is aligned and working towards common goals. 

Finally, cross-institutional collaboration offers untapped potential. Sharing best practices and pooling resources can help universities better utilise emerging tools and methods. "Having visible comparative data – including benchmarks – makes it possible to identify opportunities and risks," says Lee. “Having everyone understand the picture and how decisions are made generates productive conversations about how resources should be deployed in a constrained financial environment.”  

Professor Lee's insights highlight the transformative potential of data-driven approaches in academic workload management. With the right tools, expertise, and collaboration, universities can navigate the complexities of workload planning and create a more balanced and efficient academic environment. 

Her insights underscore the importance of visibility, collaboration, and capability building, as well as the need to overcome the challenge of distributed data. As universities continue to advance their workload planning efforts, strategies like Professor Lee’s, and products like Teaching Effort Analytics, will prove critical. But, as she notes, it’s what that data can do next that is really interesting. With these tools at their disposal, a new conversation can be had in universities about curricula innovation and strategic workforce planning, with a clear understanding of the benefits and resourcing required to deliver effectively. 

We are currently expanding our Teaching Effort Analytics programme for universities that want to innovate their planning and management of academic workloads. Contact us for more information about joining this new programme.