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:
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.
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.
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.
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.
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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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
Excerpted from: Victoria J. Marsick, Rachel Fichter, Karen E. Watkins, 2022. From Work-based Learning to Learning-based Work: Exploring the Changing Relationship between Learning and Work, in: The SAGE Handbook of Learning and Work. SAGE Publications.
Reda Sadki of The Geneva Learning Foundation (TGLF), working with Jhilmil Bahl from the World Health Organization (WHO) and funding from the Bill and Melinda Gates Foundation, developed an extraordinary approach to blending work and learning. The program started as a series of digitally offered courses for immunization personnel working in various countries, connecting in-country central planners, frontline workers, and global actors. The program was designed to address five common problems in training (Sadki, 2018): the inability to scale up to reach large audiences; the difficulty in transferring what is learned; the inability to accommodate different learners’ starting places; the need to teach learners to solve complex problems; and the inability to develop sufficient expertise in a timely way to ensure learning is greater than the rate of change (Revans, 1984).
The approach grew out of work with Scholar, an innovative learning platform, developed at the University of Illinois by Bill Cope and Mary Kalantzis. As the technology implementation of their ‘new learning’ theory, Scholar emphasized seven affordances of learning in a digital age that look at how new technologies change the way knowledge is created and how people connect and socialize (Cope & Kalantzis, 2016). The elements of the Scholar approach include: community-building functions and resources, such as dialogue area surveys and social media; and knowledge creation functions, including a collaborative publishing and critiquing space and tools such as language checkers, annotation functions, and a number of analytics including grade-level writing scores (see Figure 11.3).
Figure 11.3. Scholar pedagogy framework Source: Cope, Bill and Mary Kalantzis, “Assessment and Pedagogy in the Era of Machine-Mediated Learning,” pp. 350–74 in Education as Social Construction: Contributions to Theory, Research, and Practice, edited by Thalia Dragonas, Kenneth J. Gergen, Sheila McNamee and Eleftheria Tseliou, Chagrin Falls OH: Worldshare Books, 2015.
Learning in this digital milieu is very different, not because it is new (given decades of experience with the internet), but because of the rapid rate of change compared to traditional courses that rely on a fixed understanding of how we learn when we share physical space. Published work is often generated by the learners themselves either from their existing libraries or what they produce within the course – which may also become available to other courses; from internet searches, source documents within their work, etc. Project-based learning is not new either, but the scale, the speed, and the meaning of such connections (i.e., how they are experienced) are. Learning contributions of this kind reduce the need for subject matter experts and are both convincing and situated in real-life contexts. Complex cases demonstrate the problems at the center of the course. Group dialogue and the development of proposals to solve real problems build a shared knowledge base. Participants develop action plans of how they will address the problems that are in their workplace. Finally, peer critiquing and support enable everyone to improve their plans from whatever starting place.
Deliberate efforts are made to create a learning community using tools that are already embedded in daily practice (keeping in mind that these tools are constantly changing) and structured activities like randomized coffee trials (Soto, 2016) through which learners meet outside of class to get to know one another socially (i.e., ‘to be human together’). Learning is scaffolded by a human knowledge network (Watkins & Kim, 2018) with peer review, staff support, expert resources, and a unique Scholar alumni cadre of former students who volunteer as ‘accompanists’ to support new learners in navigating the technology and whatever else creates a barrier for novices. Peer review is based on an expert rubric and facilitated by the Scholar team. This approach is scalable, with more than 800 learners in each cohort and 400 alumni volunteering to serve as accompanists. A small project team manages multiple cohorts at a time, with a duration of six to 17 weeks, depending on the course.
Recently, the Scholar team developed the Impact Accelerator, an extension to the courses that supports the implementation of course projects and encourages participants to develop new initiatives through collaboration. The Accelerator combines weekly webinars and assemblies, regular check-ins on implementation status, and support for developing in-country teams. Participants share best practices and challenging problems, for which peers provide help, responding as a culture without requiring prompting or intervention to do so. Initial findings from an evaluation of the Accelerator indicated faster implementation of action plans and improved collaboration among participants.
Over 20 country groups formed. In a short time, alumni documented that, as a result of what they learned and implemented, immunization coverage in their region improved. Learning involves a unique blend of a traditional format – an e-learning delivery platform – and consistent and deliberate use of actual work challenges and plans to generate improved workplace performance through a combination of peer support, healthy peer competition, and mentoring and coaching.
Sadki’s approach has been called ‘magic’. He disagrees. He says: ‘Learners are transmuted into teachers, leaders, and facilitators. In some countries, learners are self-organizing to take on issues that matter to them, evolving course projects into a potentially transformative agenda.’ He says success comes ‘from modestly intersecting the science of learning with real, lived learning culture and from reframing education as philosophy for change in the Digital Age. That, and a lot of elbow grease’ (Sadki, 2019). Sadki believes that impact is possible – even tangible – when educators connect the dots among the course, the individuals, and their context. His approach combines informal and incidental learning with conscious restructuring of context. The goal of his courses is knowledge creation focused on creating change in the workplace. The approach has gained sufficient momentum that ‘Scholar’ is more a movement than a learning approach. Sadki, a lifelong social entrepreneur and activist, has invented a new approach to learning and changing individuals and organizations. Table 11.2 summarizes features of the initiative map against the framework of learning in terms of separation, coterminous, seamlessly integrated or learning based work.
Cope, B., Kalantzis, M., 2016. Conceptualizing e-Learning. Common Ground Publishing, Chicago.
Revans, R. (1984). The origins and growth of action learning. London, England: Chartwell- Bratt.
Sadki, R. (2018). Peer learning support capacity building with Scholar. Poster presented at the Teach to Reach Conference, Bill and Melinda Gates Foundation, Dar es Salaam, Tanzania.
Sadki, R. (2019). Magic. Retrieved from: https://stories.learning.foundation/2019/03/25/magic/
Siemens, G. (2007). Connectivism: Creating a learning ecology in distributed environments. In Hug, T. (Ed.). Didactics of micro- learning. Concepts, discourses and examples (pp. 53–68). Munster, Germany: Waxmann verlag GmbH.
Soto, M. (2016). A simple tool to help M&A integration: Randomized coffee trials. Retrieved from: https://blogs.harvard.edu/ msoto/2016/01/26/a-simple-tool-to-help-ma-integration-randomised-coffee-trials/
Watkins, K. & Kim, K. (2018). Current status and promising directions for research on the learning organization. Human Resource Development Quarterly, 29(1), 15–29. doi:10.1002/hrdq.21293
