Universities are very good at adding things. A problem arises, prompting us to add a process. When we identify a risk, we introduce an approval step. If something goes wrong, we implement a policy. We also add reporting requirements, committees, systems, technologies, guidelines, and procedures, often for very legitimate reasons. Higher education institutions are complex organisations that operate in dynamic environments, and addressing emerging problems is a necessary part of their function.
What we may be less good at is creating the time and space to understand what sits beneath the problem before deciding how to respond. We may also be less good at returning several years later to ask whether the solution we introduced worked, is still needed, or requires an update.
This process often establishes a familiar institutional cycle. A problem emerges as urgent, drawing significant attention. Academics and staff are then asked to respond. An intervention is created and put into action. Over time, the immediate issue recedes, and the institution shifts its focus to the next priority. The intervention, however, remains.
Over time, universities can accumulate solutions to yesterday’s problems while continually responding to today’s.
The systems thinking iceberg model offers a helpful perspective on why this occurs. It encourages us to look beyond visible events and explore the underlying patterns, structures, and mental models.
Perhaps the challenge is not that universities are incapable of going below the waterline. It is whether we give ourselves enough opportunity to stay there long enough to understand what we find.
We Respond to What We Can See
Events demand attention because they are visible. Enrolments decline. Student satisfaction falls. Attendance changes. Academic integrity cases increase. Staff workload becomes difficult to manage. A course underperforms. A new technology disrupts established practices.
These are genuine issues, and institutions can’t just ignore them during long periods of reflection. Occasionally, they require an urgent response. The issue arises when the reaction to the event is mistaken for the complete solution.
If students are not attending, we monitor attendance. If academic integrity cases increase, we strengthen detection and reporting processes. If satisfaction scores decline, we create an action plan. If staff workload becomes difficult to manage, we introduce another process for recording or allocating workload.
Any of these responses might be appropriate. But the iceberg model asks us to keep going. Rather than only asking “What happened?”, we need to ask “What keeps happening?”
What Keeps Happening?
Analysing below the event level involves identifying patterns. Where does student disengagement tend to happen repeatedly? When do cases of academic misconduct rise? Are certain assessment types consistently problematic? Do workload issues recur annually? Do similar challenges arise across various courses or faculties?
Universities have enormous amounts of data that can help answer these questions. Yet our reporting cycles can encourage us to focus on individual data points: this year’s result, this semester’s survey, this cohort’s retention or the latest KPI.
Patterns demand a broader perspective. They can also show that what looks like a new problem might actually be just another form of an existing issue. If similar problems keep emerging across various courses, cohorts, or years, addressing each one as a separate, isolated incident is unlikely to lead to significant improvement. At some point, we need to ask what is producing the pattern.
When the Institution Becomes Part of the Question
Going deeper takes us to structures. This is where systems thinking can become uncomfortable because the problem can no longer automatically be located with an individual student, academic, course or department.
If students are disengaging, consider how factors like timetabling, curriculum structure, delivery methods, and the overall student experience might influence this. If assessment issues persist, examine how assessment policies, course formats, technology use, and grading approaches could be contributing. When academic workload remains a concern, analyse which institutional expectations and procedures consistently increase it.
This makes the institution itself part of the investigation. That shift matters because event-level responses can easily focus on changing the behaviour of the people experiencing the problem. Students need to attend. Academics need to redesign. Course teams need to improve their results. Staff need to follow a new process.
Sometimes these changes are necessary, but they shouldn’t prevent us from questioning whether the larger system affects the behaviour we’re trying to change. The more deeply we investigate, the more likely we are to find questions that can’t be answered by simply applying another procedure.
What Do We Assume to Be True?
At the deepest level of the iceberg are mental models: the assumptions and beliefs that shape the structures we create. Higher education has many of them.
We might assume that attendance equals engagement, grades measure learning, and degrees should be divided into discrete units. We might assume that teaching takes place at specific times and places, that quality relies on documentation, and that academic work should follow standard organisational methods. Additionally, it’s common to think that longstanding university practices must continue simply because that’s how universities have always operated. While these assumptions aren’t necessarily wrong, they need to be made visible enough to be critically examined.
Once that happens, the conversation changes. Instead of asking how to make students attend more often, we might ask what makes attendance valuable. Instead of asking how to make grading more consistent, we might ask what grades actually tell us about learning. Instead of asking how academics can complete another process more efficiently, we might ask whether that process still needs to exist.
These questions take us well below the visible event. They also take time.
Responding Is Not the Same as Solving
This is where the experience of academics and professional staff becomes particularly important. People working within universities can find themselves moving from one urgent institutional issue to another. A new priority, challenge, or disruption requires a response. Considerable energy is invested, often by people who genuinely want to improve the institution and the student experience.
The frustration may not come from being asked to change. It can come from being asked to change without being given the opportunity to go deep enough.
Academics may recognise that an issue is more complicated than the visible event suggests. They may see connections with curriculum, assessment, policy, technology or student experience. But understanding those connections, redesigning thoughtfully, evaluating what happens and refining the response all require time.
When that time is unavailable, the result can feel like they are completing work that is essentially a band-aid fix. Something is changed because something has to be done. The immediate problem may become less visible, allowing the institution to move forward. But the structures and assumptions beneath it may remain largely untouched.
Then another problem becomes urgent.
AI Is the Current Problem. It Won’t Be the Last.
Artificial intelligence provides an obvious contemporary example. Its swift progress has rightly prompted higher education to respond. Academics are now reevaluating assessment methods, academic integrity policies, curricula, learning outcomes, and student knowledge requirements. In response, institutions have implemented new policies, provided guidance, trained staff, adopted technologies, and established new procedures. Much of this work is necessary.
But the urgency surrounding AI can also keep us close to the surface. How do we stop inappropriate AI use? How should this assessment be redesigned? What should our policy say? Which tools should students be allowed to use?
But below those questions are much larger ones. What is assessment actually for? What evidence of learning do we value? What should students learn when technologies can perform some of the tasks we previously asked students to demonstrate? What does authorship mean? What capabilities will graduates need? What assumptions about knowledge, expertise and academic integrity sit beneath our existing practices?
These questions aren’t solely about AI, but AI’s influence offers a chance to examine the topic more thoroughly. Although AI is the most pressing concern today, it won’t be the final one. Future advances in technology, regulation, finance, or society will inevitably require institutional focus. The main question is whether we will have comprehended this issue sufficiently before the next challenge arises.
When the Problem Disappears, the Solution Remains
Even when institutions invest time in crafting thoughtful responses, a recurring problem remains. Once a matter appears resolved, we often move on, which is understandable given limited institutional resources. When an issue is no longer urgent, focus and resources tend to shift elsewhere. Over time, the intervention becomes part of the normal system. This could take the form of a policy, procedure, technology, reporting requirement, committee, or accepted practice.
However, systems are not static. Student groups change, technologies advance, teaching methods evolve, staff roles adapt, regulations shift, and institutional priorities are redefined. A solution designed for a particular setting might persist well beyond that environment’s end.
The original problem may be forgotten while the solution becomes part of how the university operates. Eventually, yesterday’s solution can become today’s problem.
A process introduced for a legitimate reason may now create unnecessary workload. An approval added after a particular incident may persist for years. A policy responding to one technological environment may constrain practice in another. A reporting requirement may continue even though nobody can clearly explain which decision the information now supports.
Because these things sit below the waterline, they can remain largely invisible until their consequences become another event. And the cycle begins again.
We Are Good at Adding. What About Removing?
This may be one of the more difficult challenges for institutional improvement. Universities regularly respond to problems by adding something: a new policy, a new process, another approval, another report, another committee, another piece of technology or another responsibility. Each addition usually has a reason for its introduction. Viewed individually, each might make complete sense. Collectively, however, they accumulate.
What appears to be a simple sequence of responses to an institution can seem like a complex web of procedures and demands to academics and staff. Each response may mirror an institutional memory of a previous issue that required resolution. Therefore, institutional improvement might require both adding and subtracting elements.
When we introduce something to solve a problem, we should also ask when we will return to it. Did it work? Does the original problem still exist? Have circumstances changed? Is the intervention creating unintended consequences? Could it now be simplified? And, perhaps most importantly, do we still need it?
Going below the waterline shouldn’t happen only when something goes wrong. Sometimes we need to go looking when everything appears to be working.
Making Time to Go Below the Waterline
None of this means universities can stop responding to immediate problems. They cannot. The challenge is creating institutional structures that allow immediate responses to sit alongside deeper investigation.
That requires time. It requires people to be able to look across courses, faculties and years rather than only at the problem immediately in front of them. It requires academics and professional staff to have opportunities to investigate, experiment, evaluate and reconsider, rather than simply implement the next response.
It also requires leadership to recognise that the absence of a visible problem does not necessarily mean that a system is working well. Perhaps one of the most important questions institutional leaders can ask when presented with a problem is not simply:
“What are we going to do about it?”
It might be:
“What do we need to understand before we decide what to do about it?”
That question creates permission to move below the waterline.
There is another question that should perhaps come later:
“When are we going to come back and see whether it still works?”
What If Today’s Problem Was Yesterday’s Solution?
The iceberg model reminds us that what we see is only a small part of what is happening within a system. Universities will always have events requiring immediate responses. Another challenge, priority, or disruption will always compete for attention. AI is one of the most visible today. Something else will follow.
The challenge isn’t avoiding reactions but preventing them from becoming our main method of institutional change. When an issue arises, we should have the chance to ask what underlying patterns exist, what structures generate these patterns, and what assumptions led to these structures.
And when we develop a solution, our responsibility should not necessarily end when the immediate problem disappears. We need to come back to it. Because sometimes the problem emerging above the waterline today may have its origins in a perfectly reasonable solution we placed beneath it years ago.
Reflection Questions
- How much of our institutional change is driven by the problem that is most visible at the time?
- Are academics and professional staff given sufficient time to understand problems, or primarily expected to respond to them?
- Which recurring issues in our institutions might indicate deeper structural problems?
- What assumptions about teaching, learning and university life have become so established that we rarely question them?
- What policies, procedures or requirements were introduced to solve past problems but have rarely been reconsidered?
- When we introduce a new institutional process, do we also decide when and how to review it?
- What could we remove, rather than add, to improve our institutions?
- How can universities create genuine time and space for people to work below the waterline?