What is complex learning?

Complex learning happens when people solve real problems instead of just memorizing facts.

Think about the difference between reading about how to ride a bicycle and actually learning to ride one.

You cannot learn to ride a bicycle just by reading about it – you need to practice, fall, adjust, and try again until your body understands how to balance.

Health challenges work the same way.

Reading about how to respond to a disease outbreak is very different from actually managing one.

Complex learning recognizes this difference.

5 key features of complex learning:

1. Learning by doing: People learn best when they work on real problems they face in their jobs. Instead of just listening to experts, they actively try solutions, see what works, and adjust their approach.
2. No single right answer: Complex learning deals with situations where there is no perfect solution that works everywhere. What works in one community might fail in another because of different resources, cultures, or systems.
3. Adapting to local reality: Rather than following fixed steps, complex learning helps people adapt general principles to their specific situation. A rural clinic and an urban hospital might need different approaches even when dealing with the same disease.
4. Connecting different types of knowledge: Complex learning brings together technical knowledge (facts and procedures) with practical wisdom (experience and judgment). Both are needed to solve real health challenges.
5. Learning from mistakes: In complex learning, mistakes are valuable opportunities to learn, not failures to be hidden. When something doesn’t work, the question becomes “What can we learn from this?” rather than “Who is to blame?”

Why it matters for health work:

Most health challenges are complex problems. Disease outbreaks, vaccination campaigns, and health system improvements all require more than just technical knowledge. They require the ability to:

• Adapt to changing situations
• Work with limited resources
• Coordinate with different groups
• Solve unexpected problems
• Learn from experience

Complex learning builds these abilities by engaging people with real challenges, supporting them as they try solutions, and helping them reflect on what they learn.

Unlike traditional training that assumes knowledge flows from experts to learners, complex learning recognizes that knowledge emerges through practice and experience. When health workers engage with complex learning, they don’t just know more – they become better problem-solvers capable of addressing the unique challenges in their communities.

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What is a complex problem?

What is a complex problem and what do we need to tackle it?

Problems can be simple or complex.

Simple problems have a clear first step, a known answer, and steps you can follow to get the answer.

Complex problems do not have a single right answer.

They have many possible answers or no answer at all.

What makes complex problems really hard is that they can change over time.

They have lots of different pieces that connect in unexpected ways.

When you try to solve them, one piece changes another piece, which changes another piece.

It is hard to see all the effects of your actions.

When you do something to help, later on the problem might get worse anyway.

You have to keep adapting your ideas.

To solve really hard problems, you need to be able to:

• Think about all the puzzle pieces and how they fit, even when you don’t know what they all are.
• Come up with plans and change them when parts of the problem change.
• Think back on your problem solving to get better for next time.

The most important things are being flexible, watching how every change affects other things, and learning from experience.

Image: The Geneva Learning Foundation Collection © 2024

References

Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.

Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.

Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., Siemens, G., 2023. Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence 4, 100138. https://doi.org/10.1016/j.caeai.2023.100138

Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.

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Education as a system of systems: rethinking learning theory to tackle complex threats to our societies

In their 2014 article, Jacobson, Kapur, and Reimann propose shifting the paradigm of learning theory towards the conceptual framework of complexity science. They argue that the longstanding dichotomy between cognitive and situative theories of learning fails to capture the intricate dynamics at play. Learning arises across a “bio-psycho-social” system involving interactive feedback loops linking neuronal processes, individual cognition, social context, and cultural milieu. As such, what emerges cannot be reduced to any individual component.

To better understand how macro-scale phenomena like learning manifest from micro-scale interactions, the authors invoke the notion of “emergence” prominent in the study of complex adaptive systems. Discrete agents interacting according to simple rules can self-organize into sophisticated structures through across-scale feedback.

For instance, the formation of a traffic jam results from the cumulative behavior of individual drivers. The jam then constrains their ensuing decisions.

Similarly, in learning contexts, the construction of shared knowledge, norms, values and discourses proceeds through local interactions, which then shape future exchanges. Methodologically, properly explicating emergence requires attending to co-existing linear and non-linear dynamics rather than viewing the system exclusively through either lens.

By adopting a “trees-forest” orientation that observes both proximal neuronal firing and distal cultural evolution, researchers can transcend outmoded dichotomies. Beyond scrutinizing whether learner or environment represents the more suitable locus of analysis, the complex systems paradigm directs focus towards their multifaceted transactional synergy, which gives rise to learning. This avoids ascribing primacy to any single level, as well as positing reductive causal mechanisms, instead elucidating circular self-organizing feedback across hierarchically nested systems.

The implications are profound. Treating learning as emergence compels educators to appreciate that curricular inputs and pedagogical techniques designed based upon linear extrapolation will likely yield unexpected results. Our commonsense notions that complexity demands intricacy fail to recognize that simple nonlinear interactions generate elaborate outcomes. This epistemic shift suggests practice should emphasize creating conditions conducive for adaptive growth rather than attempting to directly implant mental structures. Specifically, adopting a complexity orientation may entail providing open-ended creative experiences permitting self-guided exploration, establishing a learning culture that values diversity, dissent and ambiguity as catalysts for sensemaking, and implementing distributed network-based peer learning.

Overall, the article explores how invoking a meta-theory grounded in complex systems science can dissolve dichotomies that have plagued the field. It compels implementing flexible, decentralized and emergent pedagogies far better aligned to the nonlinear complexity of learner development in context.

Sophisticated learning theories often fail to translate into meaningful practice. Yet what this article describes closely corresponds to how The Geneva Learning Foundation (TGLF) is actually implementing its vision of education as a philosophy for change, in the face of complex threats to our societies. The Foundation conceives of learning as an emergent phenomenon arising from interactions between individuals, their social contexts, and surrounding systems. Our programs aim to catalyze this emergence by connecting practitioners facing shared challenges to foster collaborative sensemaking. For example, our Teach to Reach events connect tens of thousands of health professionals to share experience on their own terms, in relation to their own contextual needs. This emphasis on open-ended exploration and decentralized leadership exemplifies the flexible pedagogy demanded by a complexity paradigm. Overall, the Foundation’s work – deliberately situated outside the constraints of vestigial Academy – embodies the turn towards nonlinear models that can help transcend stale dichotomies. Our practice demonstrates the concrete value of recasting learning as the product of embedded agents interacting to generate systemic wisdom greater than their individual contributions.

Jacobson, M.J., Kapur, M., Reimann, P., 2014. Towards a complex systems meta-theory of learning as an emergent phenomenon: Beyond the cognitive versus situative debate. Boulder, Colorado: International Society of the Learning Sciences. https://doi.dx.org/10.22318/icls2014.362

Illustration © The Geneva Learning Foundation Collection (2024)

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