How fair is your academic workload model?

Can institutions create clear, fair workload models that encapsulate both common standards and legitimate variation?

I come at this conversation from a place of deep involvement with curriculum design and development. My first teaching role saw me creating a year’s worth of new material for students at vastly different levels of mastery. I spent 16 hours a week in the classroom—and just about every other waking hour preparing lesson plans and meeting with staff and students.

The fact is: if you want to make a real impact on students—if you want them to learn, to be engaged—then a huge amount must happen behind the scenes to deliver a quality experience.

That being said, I do like clear standards for work and achievement. Maybe it’s because I’m a math guy and drawn to structure. Maybe it’s because I’m wired to focus on technical solutions over institutional politics—even when I know that politics often shape what’s possible. But, I like that we hold students to standards, and I like it when institutions hold themselves to standards too.

How then do we reconcile the two opposing forces of efficiency & quality in a workload model?

On one hand, there’s what many call the “race to the bottom,” where efficiency pressures degrade quality by crowding out the time and space needed to do things well. I often hear—and agree with—the maxim: “When a metric becomes a target, it’s no longer a good metric.”

But I’ve also worked in enough contexts, across enough countries, to know the truth of another saying: “Work expands to fill the time allotted.” Give a task two weeks and it’ll take two. But with focus, it can often be done in one—without any real drop in quality. We often underestimate what we can achieve with sustained, incremental progress.

So where does that leave us if we want a reasonable formula to either drive or estimate academic workload?

No institution can avoid friction while trying to pursue both quality and efficiency. So, our policies have to be smart.

One client of ours recently nailed it when reflecting on their own policies, “Once flexibility becomes contortionism, you’ve got to reset.” 

And make no mistake, this isn’t an issue of central control vs. individual agency.

The issue is this: if in practice, everyone is writing their own rules, then whatever technical problems an overly flexible policy was intended to address, that initial problem will inevitably get overshadowed by far more toxic issues of equity, fairness, transparency and accountability between divisions.

What aspects of a workload model should be flexible, and what shouldn’t?

Institutions need to communicate a unified philosophy around what drives the lion’s share of core academic work.

What drives teaching workload for individual subjects or units? Is it the year-level of the subject? Credit points? Step changes in student enrolment numbers? Linear growth in work with every additional student?

While the whole institution may not need to converge on precisely how many minutes of prep time are acceptable for each individual credit point, there does need to be a shared understanding of primary drivers.

We don’t need all faculties singing the same song, but we do need them speaking the same language.

There’s a long tail of tasks required to deliver a subject. But most institutions share a common 70–80% core of what academic work looks like. The workload model should address the core, not the tail.

Each academic division will have strong claims to why certain parameters of a model should be applied differently to them. Thier tutorial sizes may be different. They may have more labs. Their lab equipment may require more maintenance. These are all legitimate, and that is why different application of different parameters is okay—but not totally different models with different drivers.

And of course, there will always be exceptions, edge cases and anomalies, but that is why any good academic workload framework would allow for some reasonable margin of error.

This work is challenging. And it's often shaped by the success—or failure—of EBA negotiations. But benchmarking across your institution and across the sector at large can provide a powerful anchor.

So, what’s the way forward?

1.      Gather information and find out where you stand.

2.      Agree on the core 70–80% of academic workload drivers.

3.      Accept legitimate variation, but with agreed boundaries.

4.      Create clarity without forcing rigid uniformity.

And then we're left with a key question:

What would it take for your institution to commence this process and build a workload model that’s both fair and consistently applied?

If you want to continue the conversation, please reach out. It’s a topic we’re passionate about and believe must be tackled from multiple, equally important angles.