Tag: Collective Intelligence

  • Knowing-in-action: Bridging the theory-practice divide in global health

    Knowing-in-action: Bridging the theory-practice divide in global health

    The gap between theoretical knowledge and practical implementation remains one of the most persistent challenges in global health. This divide manifests in multiple ways: research that fails to address practitioners’ urgent needs, innovations from the field that never inform formal evidence systems, and capacity building approaches that cannot meet the massive scale of learning required. Donald Schön’s seminal 1995 analysis of the “dilemma of rigor or relevance” in professional practice offers crucial insights for “knowing-in-action“. It can help us understand why transforming global health requires new ways of knowing – a new epistemology.

    Listen to this article below. Subscribe to The Geneva Learning Foundation’s podcast for more audio content.

    Schön’s analysis: The dilemma of rigor or relevance

    Schön begins by examining how knowledge becomes institutionalized through education. Using elementary school mathematics as an example, he describes how knowledge is broken into discrete units (“math facts”), organized into progressive modules, assembled into curricula, and measured through standardized tests. This systematization shapes not just content but the entire organization of time, space, and institutional arrangements.

    From this foundation, Schön introduces his central metaphor of two contrasting landscapes in professional practice that prevent “knowing-in-action”. As he describes it:

    “In the varied topography of professional practice, there is a high, hard ground overlooking a swamp. On the high ground, manageable problems lend themselves to solution through the use of research-based theory and technique. In the swampy lowlands, problems are messy and confusing and incapable of technical solution.”

    The cruel irony, Schön observes, lies in the relative importance of these terrains: “The problems of the high ground tend to be relatively unimportant to individuals or to society at large, however great their technical interest may be, while in the swamp lie the problems of greatest human concern.”

    This creates what Schön calls the “dilemma of rigor or relevance” – practitioners must choose between remaining on the high ground where they can maintain technical rigor or descending into the swamp where they must rely on experience, intuition, and what he terms “muddling through.”

    The historical roots of the divide

    Schön traces this dilemma to the epistemology embedded in modern research universities. Drawing on Edward Shils’s historical analysis, he describes how American scholars returning from Germany after the Civil War brought back “the German idea of the university as a place in which to do research that contributes to fundamental knowledge, preferably through science.”

    This was, as Schön notes, “a very strange idea in 1870,” running counter to the prevailing British model of universities as sanctuaries for liberal arts or finishing schools for gentlemen. The new model first took root at Johns Hopkins University, whose president embraced the “bizarre notion that professors should be recruited, promoted, and granted tenure on the basis of their contributions to fundamental knowledge.”

    This shift created what Schön terms the “Veblenian bargain” (named after Thorstein Veblen), establishing a separation between:

    • Research universities focused on “true scholarship” and fundamental knowledge
    • Professional schools dedicated to practical training

    Knowing-in-action in global health: From fragmentation to integration

    The historical division between theory and practice that Schön identified continues to shape global health in profound and often problematic ways. This manifests in three interconnected challenges that demand our urgent attention: the knowledge-practice gap, the scale challenge, and the complexity challenge. Yet emerging approaches suggest potential paths forward, particularly through structured peer learning networks that could help bridge Schön’s “high ground” and “swamp.”

    Three fundamental challenges

    Challenge #1: The knowing-in-action divide

    The separation between research institutions and field practice creates not just an academic concern but a practical crisis in healthcare delivery. Consider the response to COVID-19: while research institutions rapidly generated new knowledge about the virus, frontline health workers struggled to translate this into practical approaches for their specific contexts. Their hard-won insights about what worked in different settings rarely made it back into formal evidence systems, epitomizing the one-way flow of knowledge that impoverishes both research and practice.

    This pattern repeats across global health. Research agendas, shaped by academic incentives and funding priorities, often fail to address practitioners’ most pressing challenges. A community health worker in rural Bangladesh facing complex challenges around vaccine hesitancy may struggle to find relevant guidance – while global experts are convinced that they already have all the answers. Meanwhile, local solutions to building vaccine confidence remain uncaptured by formal knowledge systems.

    The rise of implementation science attempts to bridge this divide, yet often remains subordinate to “pure” research in academic hierarchies. This reflects Schön’s observation about the privileging of high ground problems over swampy ones, even when the latter hold greater practical significance.

    Challenge #2: The scale imperative

    Traditional approaches to professional education face fundamental limitations in meeting the massive need for health worker capacity building. The World Health Organization projects a shortfall of 10 million health workers by 2030, mostly in low- and middle-income countries. Conventional training approaches that rely on cascading knowledge through workshops and formal courses can reach only a fraction of those who need support.

    More fundamentally, these knowledge transmission models prove inadequate for addressing complex local realities. A standardized curriculum developed by experts, no matter how well-designed, cannot anticipate the diverse challenges health workers face across different contexts. When a district immunization manager in Nigeria must adapt vaccination strategies for nomadic populations during a drought, they need more than pre-packaged knowledge – they need ways to learn from others who are facing similar challenges.

    Resource constraints further limit the reach of conventional approaches. The cost of traditional training programmes, both in money and time away from service delivery, makes it impossible to scale them to meet the need. Yet the human cost of this capacity gap, measured in preventable illness and death, demands urgent solutions.

    Challenge #3: The complexity conundrum

    Contemporary global health faces challenges that fundamentally resist standardized technical solutions. Climate change exemplifies this complexity, creating cascading effects on health systems and communities that cannot be addressed through linear interventions. When rising temperatures alter disease patterns while simultaneously disrupting cold chains for vaccine delivery, no single technical fix suffices.

    Similarly, emerging and re-emerging infectious diseases demand responses that cross traditional boundaries between animal and human health, environmental factors, and social determinants. Health workforce development must grapple with complex systemic issues around motivation, retention, and capacity building. The COVID-19 pandemic demonstrated how traditional approaches to health system strengthening often prove inadequate in the face of complex adaptive challenges.

    Emerging solutions: A new paradigm for learning and practice

    Recent innovations suggest promising approaches to bridging these divides through structured peer learning networks. Digital platforms enable health workers to share experiences and solutions across geographical boundaries, creating new possibilities for scaled learning that maintains local relevance.

    Solution #1: The power of structured peer learning

    Experience from digital learning networks demonstrates how structured peer interaction can enable more efficient and effective knowledge sharing than traditional top-down approaches. When health workers can directly connect with peers facing similar challenges, they not only share solutions but collectively generate new knowledge through their interactions.

    These networks provide mechanisms for validating practical knowledge through peer review processes that complement traditional academic validation. A successful intervention developed by a rural clinic in Thailand can be critically examined by peers, adapted for different contexts, and rapidly disseminated across the network. This creates a more dynamic and responsive knowledge ecosystem than traditional publication cycles allow.

    Solution #2: Network effects and collective intelligence

    The potential of practitioner networks extends beyond simple knowledge sharing. When properly structured, these networks create possibilities for:

    1. Rapid adaptation to emerging challenges through real-time sharing of experiences
    2. Collective problem-solving that draws on diverse perspectives and contexts
    3. Systematic capture and analysis of field innovations
    4. Development of context-specific solutions that build on shared learning

    Most importantly, these networks can help bridge Schön’s high ground and swamp by creating dialogue between different forms of knowledge and practice. They provide spaces where academic research can inform field practice while simultaneously allowing field insights to shape research agendas.

    Four principles toward knowing-in-action for global health

    Drawing on Schön’s call for a “new epistemology,” we can identify four principles for transforming how we know what we know in global health:

    Principle #1: Valuing multiple forms of knowledge

    The complexity of contemporary health challenges demands recognition of multiple valid forms of knowledge. The practical wisdom developed by a community health worker through years of service deserves attention alongside randomized controlled trials. This requires challenging existing hierarchies of evidence while maintaining rigorous standards for validating knowledge claims.

    Principle #2: Enabling knowledge creation from practice

    Health workers must be supported as knowledge producers, not just knowledge consumers. This means creating structures for systematically capturing and validating field insights, building evidence from implementation experience, and enabling continuous learning from practice. Digital platforms can provide scaffolding for this knowledge creation while ensuring quality through peer review processes.

    Principle #3: Scaling through networked learning

    Traditional scaling approaches that rely on standardization and top-down dissemination must be complemented by networked learning to create and amplify knowing-in-action. This means building systems that can:

    1. Connect practitioners across contexts and boundaries
    2. Enable peer validation of knowledge
    3. Support rapid dissemination of innovations
    4. Build collective intelligence through structured interaction

    Principle #4: Embracing complexity

    Rather than seeking to reduce complexity through standardization, health systems must build capacity for working effectively within complex adaptive systems. This means supporting adaptive learning, enabling context-specific solutions, and building capacity for systems thinking at all levels.

    The challenges facing global health today demand new ways of creating, validating, and sharing knowledge. By embracing approaches that bridge Schön’s high ground and swamp, we may find paths toward health systems that are both more rigorous and more relevant to the communities they serve.

    Looking forward

    Schön’s analysis helps explain why traditional approaches to global health knowledge and learning often fall short. More importantly, it points toward solutions that could help bridge the theory-practice divide to support knowing-in-action:

    1. New digital platforms that enable peer learning at scale
    2. Networks that connect practitioners across contexts
    3. Approaches that validate practical knowledge
    4. Systems that support rapid learning and adaptation

    Schön’s insights remain remarkably relevant to contemporary global health challenges. His call for a new epistemology that can bridge theory and practice speaks directly to our current needs. By embracing new approaches to learning and knowledge creation that honor both rigor and relevance, we may find ways to address the complex challenges that lie ahead.

    The key lies not in choosing between high ground and swamp, but in building new kinds of bridges between them – bridges that can support the massive scale of learning needed while maintaining the local relevance essential for impact. Recent innovations in peer learning networks and digital platforms suggest this bridging may be increasingly possible, offering hope for more effective global health practice in an increasingly complex world.

    The challenge now is to develop and implement these bridging approaches at the scale needed to support global health workers worldwide. This will require new ways of thinking about knowledge, learning, and practice – ways that honor both the rigor of research and the wisdom of experience. The future of global health may depend on our success in this endeavor.

    Listen to the AI podcast deep dive about this article

    Reference

    Schön, Donald A., 1995. Knowing-in-action: The new scholarship requires a new epistemology. Change: The Magazine of Higher Learning 27, 27–34. https://doi.org/10.1080/00091383.1995.10544673

    Image: The Geneva Learning Foundation Collection © 2024

  • Why become a Teach to Reach Partner?

    Why become a Teach to Reach Partner?

    We need new ways to tackle global health challenges that impact local communities.

    It is obvious that technology alone is not enough.

    We need human ingenuity, collaboration, and the ability to share across borders and boundaries.

    That is why I am excited about Teach to Reach.

    Imagine if we could tap into the collective intelligence of over 20,000 health professionals working on the front lines in low- and middle-income countries.

    What insights could we gain?

    What innovations might we uncover?

    This is exactly what Teach to Reach is doing.

    In June 2024, Teach to Reach 10 brought together 21,398 participants from across the health system – from community health workers to national policymakers.

    This diverse group represents an incredible wealth of knowledge and experience that has often been overlooked in global health decision-making.

    Bridge the gap between policy and practice

    One of the most exciting aspects of Teach to Reach is how it bridges the gap between policy and practice.

    Too often, there is a disconnect between those making decisions at the global level and those implementing programs on the ground.

    Teach to Reach creates a direct line of communication, allowing frontline workers to influence policy and program design in real-time.

    This approach not only leads to more effective interventions but also empowers health workers, increasing their engagement and motivation.

    Scale knowledge transfer and translation efficiently

    In global health, we are always looking for ways to scale solutions efficiently.

    This scaling effect is particularly crucial in low-resource settings, where formal learning opportunities may be limited.

    Teach to Reach applies this principle to peer learning.

    Then there is speed.

    The platform can disseminate best practices and local solutions much more rapidly than traditional top-down approaches.

    There is also the “know-do” gap or the “applicability problem”.

    Teach to Reach supports continuous learning by sharing experience, focused on how to get results, especially at the local community level.

    Measuring impact and driving innovation

    The Teach to Reach platform uses a comprehensive framework to track the value of participation for individuals and the benefits for partners.

    But we do not stop there.

    Teach to Reach is just one component in the Geneva Learning Foundation’s model to support new learning and leadership to drive change.

    We then track and measure what participants do with the knowledge gained and the experiences shared.

    We do this all the way to the time where improved health outcomes can be attributed to a discovery or significant learning made at Teach to Reach.

    Moreover, Teach to Reach serves as an innovation hub, surfacing diverse ideas and solutions from the field.

    For organizations looking to drive innovation in their global health programs, this platform offers a new path to creative problem-solving with those closest to the challenges.

    A call to action for global health leaders

    If you are a leader in the global health space, I urge you to consider partnering with Teach to Reach.

    Here are 5 ways in which partners have found utility in Teach to Reach:

    1. Inform a strategy with ground-level insights.
    2. Expand reach across multiple countries and health system levels.
    3. Tap into a diverse pool of local solutions – and help augment and scale them.
    4. Demonstrate commitment to supporting locally-led, community-based positive change.
    5. Accelerate progress towards global health goals through collaborative learning.

    In today’s interconnected world, our ability to solve global health challenges depends on our capacity to learn from one another and scale effective solutions quickly.

    Teach to Reach is pioneering a new approach that harnesses the power of peer learning to do just that.

    Investing in Teach to Reach can help unlock the full potential of our global health workforce and make significant strides towards a healthier, more equitable world.

    The future of global health is collaborative.

    Teach to Reach provides a way to turn the rhetoric of collaboration into practical action.

  • How to overcome limitations of expert-led fellowships for global health

    How to overcome limitations of expert-led fellowships for global health

    Coaching and mentoring programs sometimes called “fellowships” have been upheld as the gold standard for developing leaders in global health.

    For example, a fellowship in the field of immunization was recently advertised in the following manner.

    • Develop your skills and become an advocate and leader: The fellowship will begin with two months of weekly mandatory live engagements led by [global] staff and immunization experts around topics relating to rebuilding routine immunization, including catch-up vaccination, integration and life course immunization. […]
    • Craft an implementation plan: Throughout the live engagement series, fellows will develop, revise and submit a COVID-19 recovery strategic plan.
    • Receive individualized mentoring: Participants with strong plans will be considered for a mentorship program to work 1:1 with experts in the field to further develop and implement their strategies and potentially publish their case studies.

    We will not dwell here on the ‘live engagements’, which are expert-led presentations of technical knowledge. We already know that such ‘webinars’ have very limited learning efficacy, and unlikely impact on outcomes. (This may seem like a harsh statement to global health practitioners who have grown comfortable with webinars, but it is substantiated by decades of evidence from learning science research.)

    On the surface, the rest of the model sounds highly effective, promising personalized attention and expert guidance.

    The use of a project-based learning approach is promising, but it is unclear what support is provided once the implementation plan has been crafted.

    It is when you consider the logistical aspects that the cracks begin to show.

    The essence of traditional coaching lies in the quality of the one-to-one interaction, making it an inherently limited resource.

    Take, for example, a fellowship programme where interest outstrips availability—say, 1,600 aspiring global health leaders are interested, but only 30 will be selected for one-on-one mentoring.

    Tailored, one-on-one coaching can be incredibly effective in small, controlled environments.

    While these 30 may receive an invaluable experience, what happens to those left behind?

    There is an ‘elitist spiral’.

    Coaching and mentoring, while intensive, remain exclusive by design, limited to the select few.

    This not only restricts scale but also concentrates knowledge among the selected group, perpetuating hierarchies.

    Those chosen gain invaluable support.

    The majority left out are denied access and implicitly viewed as passive recipients rather than partners in a collective solution.

    Doubling the number of ‘fellows’ only marginally improves this situation.

    Even if the mentor pool were to grow exponentially, the personalized nature of the engagement limits the rate of diffusion.

    When we step back and look at the big picture, we realize there is a problem: these programs are expensive and difficult to scale.

    And, in global health, if it does not scale, it is not solving the problem.

    How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

    Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

    So while these programs can make a real difference for a small group of people, they are unlikely to move the needle on a global scale.

    That is like trying to fill a swimming pool with a teaspoon—you might make some progress, but you will never get the job done.

    The model creates a paradox: the attributes making it effective for individuals intrinsically limit systemic impact.

    There is another paradox related to complexity.

    Global health issues are inextricably tied to cultural, political and economic factors unique to each country and community.

    Complex problems require nuanced solutions.

    Yet coaching promotes generalized expertise from a few global, centralized institutions rather than fostering context-specific knowledge.

    Even the most brilliant, experienced coach or mentor cannot single-handedly impart the multifaceted understanding needed to drive impact across diverse settings.

    A ‘fellowship’ structure also subtly perpetuates the existing hierarchies within global health.

    It operates on the tacit assumption that the necessary knowledge and expertise reside in certain centralized locations and among a select cadre of experts.

    This sends an implicit message that knowledge flows unidirectionally—from the seasoned experts to the less-experienced practitioners who are perceived as needing to be “coached.”

    Learn more: How does peer learning compare to expert-led coaching ‘fellowships’?

    Peer learning: Collective wisdom, collective progress

    In global health, no one individual or institution can be expected to possess solutions for all settings.

    Sustainable change requires mobilizing collective intelligence, not just centralized expertise.

    Learn more: The COVID-19 Peer Hub as an example of Collective Intelligence (CI) in practice

    This means transitioning from hierarchical, top-down development models to flexible platforms amplifying practitioners’ contextual insights.

    The gap between need and availability of quality training in global health is too vast for conventional approaches to ever bridge alone.

    Instead of desperately chasing an asymptote of expanding elite access, we stand to gain more by embracing approaches that democratize development.

    Complex challenges demand platforms unleashing collective wisdom through collaboration. The technologies exist.

    In the “fellowship” example, less than five percent of participants were selected to receive feedback from global experts.

    A peer learning platform can provide high-quality peer feedback for everyone.

    • Such a platform democratizes access to knowledge and disrupts traditional hierarchies.
    • It also moves away from the outdated notion that expertise is concentrated in specific geographical or institutional locations.

    What learning science underpins peer learning for global health? Watch this 14-minute presentation at the 2023 annual meeting of the American Society for Tropical Medicine and Hygiene (ASTMH).

    What about the perceived trade-off between quality and scale?

    Effective digital peer learning platforms negate this zero-sum game.

    Research on MOOCs (massive open online courses) has conclusively demonstrated that giving and receiving feedback to and from three peers through structured, rubric-based peer review, achieves reliability comparable, when properly supported, to that of expert feedback alone.

    If we are going to make a dent in the global health crises we face, we have to shift from a model that relies on the expertise of the few to one that harnesses the collective wisdom of the many.

    • Peer learning isn’t a Band-Aid. It is an innovative leap forward that disrupts the status quo, and it’s exactly what the global health sector needs.
    • Peer learning is not just an incremental improvement. It is a seismic shift in the way we think about learning and capacity-building in global health.
    • Peer learning is not a compromise. It is an upgrade. We move from a model of scarcity, bound by the limits of individual expertise, to one of collective wisdom.
    • Peer learning is more than just a useful tool. It is a challenge to the traditional epistemology of global health education.

    Read about a practical example: Movement for Immunization Agenda 2030 (IA2030): grounding action in local realities to reach the unreached

    As we grapple with urgent issues in global health—from pandemic recovery to routine immunization—it is clear that we need collective intelligence and resource sharing on a massive scale.

    And for that, we need to move beyond the selective, top-down models of the past.

    The collective challenges we face in global health require collective solutions.

    And collective solutions require us to question established norms, particularly when those norms serve to maintain existing hierarchies and power imbalances.

    Now it is up to us to seize this opportunity and move beyond outmoded, hierarchical models.

    There is a path – now, not tomorrow – to truly democratize knowledge, make meaningful progress, and tackle the global health challenges that confront us all.

  • How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

    How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

    By connecting practitioners to learn from each other, peer learning facilitates collaborative development. ow does it compare to expert-led coaching and mentoring “fellowships” that are seen as the ‘gold standard’ for professional development in global health?

    Scalability in global health matters. (See this article for a comparison of other aspects.)

    Simplified mathematical modeling can compare the scalability of expert coaching (“fellowships”) and peer learning

    Let N be the total number of learners and M be the number of experts available. Assuming that each expert can coach K learners effectively:

    $latex \text{Total Number of Coached Learners} = M \times K&s=3$

    For N>>M×KN>>M×K, it is evident that expert coaching is costly and difficult to scale.

    Expert coaching “fellowships” require the availability of experts, which is often optimistic in highly specialized fields.

    The number of learners (N) greatly exceeds the product of the number of experts (M) and the capacity per expert (K).

    Scalability of one-to-one peer learning

    By comparison, peer learning turns the conventional model on its head by transforming each learner into a potential coach who can provide peer feedback.

    This has significant advantages in scalability.

    Let N be the total number of learners. Assuming a peer-to-peer model, where each learner can learn from any other learner:

    $latex \text{Total Number of Learning Interactions} = \frac{N \times (N – 1)}{2}&s=3$

    $latex \text{The number of learning interactions scales with: } O(N^2)&s=3$

    In this context, the number of learning interactions scales quadratically with the number of learners. This means that if the number of learners doubles, the total number of learning interactions increases by a factor of four. This quadratic relationship highlights the significant increase in interactions (and potential scalability challenges) as more learners participate in the model.

    However, this one-to-one model is difficult to implement: not every learner is going to interact with every other learner in meaningful ways.

    A more practical ‘triangular’ peer learning model with no upper limit to scalability

    In The Geneva Learning Foundation’s peer learning model, learners give feedback to three peers, and receive feedback from three peers. This is a structured, time-bound process of peer review, guided by an expert-designed rubric.

    When each learner gives feedback to 3 different learners and receives feedback from 3 different learners, the model changes significantly from the one-to-one model where every learner could potentially interact with every other learner. In this specific configuration, the total number of interactions can be calculated based on the number of learners N, with each learner being involved in 6 interactions (3 given + 3 received).

    The total number of interactions per learner is six. However, since each interaction involves two learners (the giver and the receiver of feedback), we do not need to double-count these interactions for the total count in the system. Hence, the total number of interactions for each learner is directly 6, without further adjustments for double-counting.

    Therefore, the total number of learning interactions in the system can be represented as:

    $latex \text{Total Number of Learning Interactions} = N \times 3&s=3$

    Given this setup, the complexity or scalability of the system in terms of learning interactions relative to the number of participants N is linear. This is because the total number of interactions increases directly in proportion to the number of learners. Thus, the Big O notation would be:

    $latex O(N)&s=3$

    This indicates that the total number of learning interactions scales linearly with the number of learners. In this configuration, as the number of learners increases, the total number of interactions increases at a linear rate, which is more scalable and manageable than the quadratic rate seen in the peer-to-peer model where every learner interacts with every other learner. Learn more: There is no scale.

    Illustration: The Geneva Learning Foundation © 2024

  • The COVID-19 Peer Hub as an example of Collective Intelligence (CI) in practice

    The COVID-19 Peer Hub as an example of Collective Intelligence (CI) in practice

    A new article by colleagues at the Cambridge Digital Education Futures Initiative (DEFI) illustrates academic understanding of Collective Intelligence (CI) through the COVID-19 Peer Hub, a peer learning initiative organized by over 6,000 frontline health workers in Africa, Asia, and Latin America, with support from The Geneva Learning Foundation (TGLF), in response to the initial shock of the pandemic on immunization services that placed 80 million children at risk of missing lifesaving vaccines. Learn more about the COVID-19 Peer Hub

    From the abstract:

    Collective Intelligence (CI) is important for groups that seek to address shared problems.

    CI in human groups can be mediated by educational technologies.

    The current paper presents a framework to support design thinking in relation to CI educational technologies.

    Our Collective Intelligence framework is grounded in an organismic-contextualist developmental perspective that orients enquiry to the design of increasingly complex and integrated CI systems that support coordinated group problem solving behaviour.

    We focus on pedagogies and infrastructure and we argue that project-based learning provides a sound basis for CI education, allowing for different forms of CI behaviour to be integrated, including swarm behaviour, stigmergy, and collaborative behaviour.

    We highlight CI technologies already being used in educational environments while also pointing to opportunities and needs for further creative designs to support the development of CI capabilities across the lifespan.

    We argue that Collective Intelligence education grounded in dialogue and the application of CI methods across a range of project-based learning challenges can provide a common bridge for diverse transitions into public and private sector jobs and a shared learning experience that supports cooperative public-private partnerships, which can further reinforce advanced human capabilities in system design.

    Article excerpt:

    As an example of Collective Intelligence in practice, in 2020–2021, more than 6000 health workers joined The Geneva Learning Foundation (TGLF) COVID-19 Peer Hub.

    Participants shared more than 1200 ideas or practices for managing the pandemic in their contexts within 10 days. Relevant peer ideas and practices were then referenced as participants produced individual, context-specific action plans that were then reviewed by peers before finalisation and implementation.

    Mapping of action plan citations (C3L 2022) demonstrate patterns of peer learning, between countries, organisations and system levels.

    In parallel, TGLF synthesises data generated by peer learners in formats legitimised by the global health knowledge system (e.g. Moore et al. 2022).

    The biggest challenge to CI in this context remains one of legitimacy: how can collective intelligence compete with the perceived gold standard of academic publication within this expert-led culture?

    We argue that as CI education is further developed and extends across the lifespan from school learning environment to work and organisational environments, CI technologies and practices will be further developed, evaluated, and refined and will gain legitimacy as part of broader societal capabilities in CI that are cultivated and reinforced on an ongoing basis.

    References

    • Kovanovic, V. et al. (2022) The power of learning networks for global health: The Geneva Learning Foundation COVID-19 Peer Hub Project Evaluation Report. Centre for Change and Complexity in Learning.
    • Moore, Katie, Barbara Muzzulini, Tamara Roldán, Juliet Bedford, and Heidi Larson. 2022. Overcoming barriers to vaccine acceptance in the community: Key learning from the experiences of 734 frontline health workers (1.0). The Geneva Learning Foundation. https://doi.org/10.5281/zenodo.6965355
    • Hogan, M.J., Barton, A., Twiner, A., James, C., Ahmed, F., Casebourne, I., Steed, I., Hamilton, P., Shi, S., Zhao, Y., Harney, O.M., Wegerif, R., 2023. Education for collective intelligence. Irish Educational Studies 1–30. https://doi.org/10.1080/03323315.2023.2250309
  • Digital challenge-based learning in the COVID-19 Peer Hub

    A digital human knowledge and action network of health workers: Challenging established notions of learning in global health

    When Prof Rupert Wegerif introduced DEFI in his blog post, he argued that recent technologies will transform the notions and practice of education. The Geneva Learning Foundation (TGLF) is demonstrating this concept in the field of global health, specifically immunization, through the ongoing engagement of thousands of health workers in digital peer learning.

    As images of ambulance queues across Europe filled TV screens in 2020, another discussion was starting: how would COVID-19 affect countries with weaker health systems but more experience in facing epidemic outbreaks?

    In the global immunization community, there were early signs that ongoing efforts to protect children from vaccine preventable diseases – measles, polio, diphtheria – would suffer. On the ground, there were early reports of health workers being afraid to work, being excluded by communities, or having key supplies disrupted. The TGLF quickly realised it had a role to play in ensuring that routine immunization would carry on in the Global South during the pandemic and then to prepare for COVID-19 vaccine introduction.

    Peer learning vs hierarchical, transmissive learning models

    Since 2016, TGLF had been slowly gaining traction in the world of immunization learning, with its digital peer learning programmes for immunization staff. These programmes reached around 15,000 people in their first four years, before the pandemic, about 70% of whom were from West and Central Africa, and about 50% of whom work at the lowest levels of health systems: health facilities and districts.

    The TGLF peer learning programmes were developed as an alternative to hierarchical, transmissive learning models, in which knowledge is developed centrally, translated into guidance by global experts, which is then disseminated through cascade training.

    In the hierarchical model, health workers are merely consumers at the periphery of the process. COVID-19 brought the inadequacies of this approach into sharper focus, as health workers dealt with challenges that had not been foreseen or processed through existing guidance.

    No technical guidance could address every scenario health workers faced, such as reaching the most marginalised communities or engaging terrified parents at a time when science had few reassuring answers. They needed to be creative and empowered to find their own solutions. Health professionals learned to rely on each other as peers, learning from each other how to negotiate many unknowns, without waiting for the answers provided by formal science.

    The TGLF approach quickly demonstrated its usefulness in connecting peers during the pandemic. In 2020, the number of platform users doubled to 30,000 in just six months (compared to four years to gain the first 15,000 users) and has now trebled to 45,000.

    Adoption doubled from 15,000 pre-pandemic users to 30,000 users in the first six months of the pandemic. It now stands at 45,000 in 2022. 

    Addressing Covid-19 impacts through challenge-based learning

    The foundation of the TGLF approach was the COVID-19 Peer Hub, an 8-month project based on challenge-based learning, which challenged individuals to give and receive feedback as they collaborated to:

    • Identify a real challenge that they were expected to address in their everyday work
    • Carry out situation analysis, and
    • Develop action plans that are peer-reviewed and improved.

    The Peer Hub was inspired by the works of several of academics who helped create the Foundation: Bill Cope and Mary Kalantzis, and their technological implementation of “New Learning;” George Siemens’ learning theory of connectivism; and Karen E. Watkins and Victoria Marsick’s insights into the significance of incidental and informal learning.

    The Peer Hub demonstrated the creation of a “human knowledge and action network” formed through both formal and informal peer learning combined with ongoing informal social learning between participants. The network was built on the principle that participants were themselves experts in their own contexts, and creators, rather than consumers, of knowledge. Front-line health workers suddenly had the legitimacy and ability to share experiences with their peers and experts from around the globe.

    Screenshot showing ten user-generated posts displayed as two rows of colourful tiles

    In the first ten days, COVID-19 Peer Hub participants shared 1224 ideas and practices through the Ideas Engine, an online innovation management tool.

    Results of peer-led, challenge-based learning interventions

    More than 6,000 health workers joined the TGLF COVID-19 Peer Hub, where they:

    Assessing the value of peer-led learning in a global vaccine education programme

    The next challenge for TGLF was how to document and capture the value of this? Most of what was shared between peers was not new or innovative at a global level – but this did not make it less useful to the individual practitioner who had not encountered it before. How to account for the sense of identity, community and solidarity arising from peer learning that gives health workers the confidence and motivation to try new things? How to make a link between investment in peer learning, and children immunized?

    “Participation in the Peer Hub has motivated me to organize my district to implement actions developed. It has also encouraged me to invite many Immunization Officers to learn the experiences from other countries to improve country immunization sessions” 

    Peer Hub participant

    Global map with lines connecting countries where participants interacted

    Tracking movement of practices and ideas shared through the Ideas Engine between countries

    Because while health workers responded positively to opportunities to connect, learn and lead with one another, TGLF is very much a new entrant in a well-established institutional learning environment for global health. Here are some questions we’ve developed as TGLF challenges established norms and ways of working:

    • How would you feel as a global expert if you were asked to give up your role as ‘sage on the stage’ to be a ‘guide on the side’ to thousands of health workers?
    • Can self-reported data from thousands of health workers evaluated by peers be trusted more or less than a peer-reviewed study?
    • What does ubiquitous digital access mean for training programmes that have previously incentivised learner participation in face-to-face events through payment?

    “I can actually broaden my vision and be more imaginative, creative towards new ideas that have come up to improve overall immunization coverage.” Peer Hub participant

    Working with DEFI and other similar institutions, TGLF looks forward to:

    ­We look forward to fruitful dialogues!

    Ian Steed, Associate, Hughes Hall
    Ian works as a consultant in the international humanitarian and development sector, focusing on the policy and practice of ‘localising’ international aid. In addition to his work with TGLF, Ian is involved with financial sustainability in the Red Cross Red Crescent Movement and is founder and board member of the Cambridge Humanitarian Centre (now the Centre for Global Equality). He studied German and Dutch at Jesus College, Cambridge, and has lived and worked in Germany and Switzerland.