#learningStrategy

2025-03-17

What is networked learning?

Networked learning happens when people learn through connections with others facing similar challenges. Think about how market traders learn their business – not through formal classes, but by connecting with other traders, sharing tips, and learning from each other’s experiences. This natural way of learning through relationships is what networked learning tries to support.

5 key features of networked learning:

  1. Learning from peers: In networked learning, people learn as much or more from others doing similar work as they do from experts. A community health worker in one village might discover an effective way to increase vaccination rates that could help workers in other villages.
  2. Knowledge flows in all directions: Unlike traditional training where knowledge flows only from the top down, networked learning allows knowledge to move in all directions – from national programs to local clinics, between regions, and from local implementers up to policy makers.
  3. Connections create value: The relationships between people become valuable resources for solving problems. Having a network of colleagues to ask for advice or share experiences with helps everyone work more effectively.
  4. Crossing boundaries: Networked learning connects people who might not normally work together – like doctors, nurses, community health workers, and managers. These diverse connections bring together different perspectives and create new solutions.
  5. Building on existing relationships: People already learn from colleagues they trust. Networked learning strengthens these natural connections and creates new ones, expanding who people can learn from.

Why networked learning matters for health work:

Health systems are full of isolated practitioners who could benefit from each other’s knowledge:

  • A nurse who developed an effective patient education approach
  • A community health worker who found a way to reach remote households
  • A clinic manager who improved medicine supply systems
  • A doctor who adapted treatment guidelines for local conditions

Networked learning connects these isolated pockets of knowledge, allowing good ideas to spread and adapt across different contexts.

Unlike traditional training that pulls people away from their work for workshops, networked learning happens through ongoing connections that support everyday problem-solving. When health workers participate in networked learning, they gain access to a community of practice that continues to provide support long after formal training ends.

Networked learning doesn’t replace expertise, but it recognizes that valuable knowledge exists throughout the health system – not just at the top. By connecting this distributed knowledge, networked learning helps good practices spread more quickly and adapt more effectively to local needs.

#globalHealth #learningStrategy #networkedLearning

What is networked learning
2025-03-15

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.

#complexLearning #complexProblems #learningStrategy #pedagogy #wickedProblems

Complex problems
2024-11-19

Why answer Teach to Reach Questions?

Have you ever wished you could talk to another health worker who has faced the same challenges as you? Someone who found a way to keep helping people, even when things seemed impossible? That’s exactly the kind of active learning that Teach to Reach Questions make possible. They make peer learning easy for everyone who works for health.

What are Teach to Reach Questions?

Once you join Teach to Reach (what is it?), you’ll receive questions about real-world challenges that matter to health professionals.

How does it work?

  1. You choose what to share: Answer only questions where you have actual experience. No need to respond to everything – focus on what matters to you.
  2. Share specific moments: Instead of general information, we ask about real situations you’ve faced. What exactly happened? What did you do? How did you know it worked?
  3. Learn from others: Within weeks, you’ll receive a collection of experiences shared by health workers from over 70 countries. See how others solved problems similar to yours.

What’s different about these questions?

Unlike typical surveys that just collect data, Teach to Reach Questions are active learning that:

  • Focus on your real-world experience.
  • Help you reflect on what worked (and what didn’t).
  • Connect you to solutions from other health workers.
  • Give back everything shared to help everyone learn.

See what we give back to the community. Get the English-language collection of Experiences shared from Teach to Reach 10. The new compendium includes over 600 health worker experiences about immunisation, climate change, malaria, NTDs, and digital health. A second collection of more than 600 experiences shared by French-speaking participants is also available.

What’s in it for you?

Peer learning happens when we learn from each other. Your answers can help others – and their answers can help you.

  1. Get recognized: You’ll be honored as a Teach to Reach Contributor and receive certification.
  2. Learn practical solutions: See how other health workers tackle challenges like yours.
  3. Make connections: At Teach to Reach, you’ll meet others who have been sharing and learning about the same issues.
  4. Access support: Global partners will share how they can support solutions you and other health workers develop.

A health worker’s experience

Here is what on community health worker from Kenya said:

“When flooding hit our area, I felt so alone trying to figure out how to keep helping people. Through Teach to Reach, I learned that a colleague in another country had faced the same problem. Their solution helped me prepare better for the next flood. Now I’m sharing my experience to help others.”

Think about how peer learning could help you when more than 23,000 health professionals are asked to share their experience on a challenge that matters to you.

Ready to start?

  1. Request your invitation to Teach to Reach now.
  2. Look for questions in your inbox.
  3. Share your experience on topics you know about.
  4. Receive the complete collection of shared experiences.
  5. Join us in December to meet others face-to-face.

Remember: Your experience, no matter how small it might seem to you, could be exactly what another health worker needs to hear.

The sooner you join, the more you’ll learn from colleagues worldwide.

Together, we can turn what each of us knows into knowledge that helps everyone.

Listen to the Teach to Reach podcast:

Is your organisation interested in learning from health workers? Learn more about becoming a Teach to Reach partner.

Image: The Geneva Learning Foundation Collection © 2024

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Why answer Teach to Reach Questions-small
2024-10-30

In a rural health center in Kenya, a community health worker develops an innovative approach to reaching families who have been hesitant about vaccination.

Meanwhile, in a Brazilian city, a nurse has gotten everyone involved – including families and communities – onboard to integrate information about HPV vaccination into cervical cancer screening.

These valuable insights might once have remained isolated, their potential impact limited to their immediate contexts.

But through Teach to Reach – a peer learning platform, network, and community hosted by The Geneva Learning Foundation – these experiences become part of a larger tapestry of knowledge that transforms how health workers learn and adapt their practices worldwide.

Since January 2021, the event series has grown to connect over 21,000 health professionals from more than 70 countries, reaching its tenth edition with 21,398 participants in June 2024.

Scale matters, but this level of engagement begs the question: how and why does it work?

The challenge in global health is not just about what people need to learn – it is about reimagining how learning happens and gets applied in complex, rapidly-changing environments to improve performance, improve health outcomes, and prepare the next generation of leaders.

Traditional approaches to professional development, built around expert-led training and top-down knowledge transfer, often fail to create lasting change.

They tend to ignore the rich knowledge that exists in practice – what we know when we are there every day, side-by-side with the community we serve – and the complex ways that learning actually occurs in professional networks and communities.

Teach to Reach is one component in The Geneva Learning Foundation’s emergent model for learning and change.

This article describes the pedagogical patterns that Teach to Reach brings to life.

A new vision for digital-first, networked professional learning

Teach to Reach represents a shift in how we think about professional learning in global health.

Its pedagogical pattern draws from three complementary theoretical frameworks that together create a more complete understanding of how professionals learn and how that learning translates into improved practice.

At its foundation lies Bill Cope’s and Mary Kalantzis’s New Learning framework, which recognizes that knowledge creation in the digital age requires new approaches to learning and assessment.

Teach to Reach then integrates insights from Watkins and Marsick’s research on the strong relationship between learning culture (a measure of the capacity for change) and performance and George Siemens’s learning theory of connectivism to create something syncretic: a learning approach that simultaneously builds individual capability, organizational capacity, and network strength.

Active knowledge making

The prevailing model of professional development often treats learners as empty vessels to be filled with expert knowledge.

Drawing from constructivist learning theory, it positions health workers as knowledge creators rather than passive recipients.

When a community health worker in Kenya shares how they’ve adapted vaccination strategies for remote communities, they are not just describing their work – they’re creating valuable knowledge that others can learn from and adapt.

The role of experts is even more significant in this model: experts become “Guides on the side”, listening to challenges and their contexts to identify what expert knowledge is most likely to be useful to a specific challenge and context.

(This is the oft-neglected “downstream” to the “upstream” work that goes into the creation of global guidelines.)

This principle manifests in how questions are framed.

Instead of asking “What should you do when faced with vaccine hesitancy?” Teach to Reach asks “Tell us about a time when you successfully addressed vaccine hesitancy in your community.” This subtle shift transforms the learning dynamic from theoretical to practical, from passive to active.

Collaborative intelligence

The concept of collaborative intelligence, inspired by social learning theory, recognizes that knowledge in complex fields like global health is distributed across many individuals and contexts.

No single expert or institution holds all the answers.

By creating structures for health workers to share and learn from each other’s experiences, Teach to Reach taps into what cognitive scientists call “distributed cognition” – the idea that knowledge and understanding emerge from networks of people rather than individual minds.

This plays out practically in how experiences are shared and synthesized.

When a nurse in Brazil shares their approach to integrating COVID-19 vaccination with routine immunization, their experience becomes part of a larger tapestry of knowledge that includes perspectives from diverse contexts and roles.

Metacognitive reflection

Metacognition – thinking about thinking – is crucial for professional development, yet it is often overlooked in traditional training.

Teach to Reach deliberately builds in opportunities for metacognitive reflection through its question design and response framework.

When participants share experiences, they are prompted not just to describe what happened, but to analyze why they made certain decisions and what they learned from the experience.
This reflective practice helps health workers develop deeper understanding of their own practice and decision-making processes.

It transforms individual experiences into learning opportunities that benefit both the sharer and the wider community.

Recursive feedback

Learning is not linear – it is a cyclical process of sharing, reflecting, applying, and refining.

Teach to Reach’s model of recursive feedback, inspired by systems thinking, creates multiple opportunities for participants to engage with and build upon each other’s experiences.

This goes beyond communities of practice, because the community component is part of a broader, dynamic and ongoing process.

Executing a complex pedagogical pattern

The pedagogical pattern of Teach to Reach come to life through a carefully designed implementation framework over a six-month period, before, during, and after the live event.

This extended timeframe is not arbitrary – it is based on research showing that sustained engagement over time leads to deeper learning and more lasting change than one-off learning events.
The core of the learning process is the Teach to Reach Questions – weekly prompts that guide participants through progressively more complex reflection and sharing.

These questions are crafted to elicit not just information, but insight and understanding.

They follow a deliberate sequence that moves from description to analysis to reflection to application, mirroring the natural cycle of experiential learning.

Communication as pedagogy

In Teach to Reach, communication is not just about delivering information – it is an integral part of the learning process.

The model uses what scholars call “pedagogical communication” – communication designed specifically to facilitate learning.

This manifests in several ways:

  • Personal and warm tone that creates psychological safety for sharing
  • Clear calls to action that guide participants through the learning process
  • Multiple touchpoints that reinforce learning and maintain engagement
  • Progressive engagement that builds complexity gradually

Learning culture and performance

Watkins and Marsick’s work helps us understand why Teach to Reach’s approach is so effective.

Learning culture – the set of organizational values, practices, and systems that support continuous learning – is crucial for translating individual insights into improved organizational performance.

Teach to Reach deliberately builds elements of strong learning cultures into its design.

Furthermore, the Geneva Learning Foundation’s research found that continuous learning is the weakest dimension of learning culture in immunization – and probably global health.

Hence, Teach to Reach itself provides a mechanism to strengthen specifically this dimension.

Take the simple act of asking questions about real work experiences.

This is not just about gathering information – it’s about creating what Watkins and Marsick call “inquiry and dialogue,” a fundamental dimension of learning organizations.

When health workers share their experiences, they are not just describing what happened.

They are engaging in a form of collaborative inquiry that helps everyone involved develop deeper understanding.

Networks of knowledge

George Siemens’s connectivism theory provides another crucial lens for understanding Teach to Reach’s effectiveness.

In today’s world, knowledge is not just what is in our heads – it is distributed across networks of people and resources.

Teach to Reach creates and strengthens these networks through its unique approach to asynchronous peer learning.

The process begins with carefully designed questions that prompt health workers to share specific experiences.

But it does not stop there.

These experiences become nodes in a growing network of knowledge, connected through themes, challenges, and solutions.

When a health worker in India reads about how a colleague in Nigeria addressed a particular challenge, they are not just learning about one solution – they are becoming part of a network that makes everyone’s practice stronger.

From theory to practice

What makes Teach to Reach particularly powerful is how it fuses multiple theories of learning into a practical model that works in real-world conditions.

The model recognizes that learning must be accessible to health workers dealing with limited connectivity, heavy workloads, and diverse linguistic and cultural contexts.

New Learning’s emphasis on multimodal meaning-making supports the use of multiple communication channels ensuring accessibility.

Learning culture principles guide the creation of supportive structures that make continuous learning possible even in challenging conditions.

Connectivist insights inform how knowledge is shared and distributed across the network.

Creating sustainable change

The real test of any learning approach is whether it creates sustainable change in practice.

By simultaneously building individual capability, organizational capacity, and network strength, it creates the conditions for continuous improvement and adaptation.

Health workers do not just learn new approaches – they develop the capacity to learn continuously from their own experience and the experiences of others.

Organizations do not just gain new knowledge – they develop stronger learning cultures that support ongoing innovation.

And the broader health system gains not just a collection of good practices, but a living network of practitioners who continue to learn and adapt together.

Looking forward

As global health challenges have become more complex, the need for more effective approaches to professional learning becomes more urgent.

Teach to Reach’s pedagogical model, grounded in complementary theoretical frameworks and proven in practice, offers valuable insights for anyone interested in creating impactful professional learning experiences.

The model suggests that effective professional learning in complex fields like global health requires more than just good content or engaging delivery.

It requires careful attention to how learning cultures are built, how networks are strengthened, and how individual learning connects to organizational and system performance.

Most importantly, it reminds us that the most powerful learning often happens not through traditional training but through thoughtfully structured opportunities for professionals to learn from and with each other.

In this way, Teach to Reach is a demonstration of what becomes possible when we reimagine how professional learning happens in service of better health outcomes worldwide.

Image: The Geneva Learning Foundation Collection © 2024

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The pedagogy of Teach to Reach
2024-10-22

Learning efficiency just got a serious boost. Check out the 3-step learning flow that can double your productivity in this incredible summary! #learningstrategy #productivityhacks #softwaredevelopment kevin.the.li/posts/learning-to

2024-08-27

It was James Gleick who noted in his book “Faster: The Acceleration of Just About Everything” the societal shift towards valuing speed over depth:

“We have become a quick-reflexed, multitasking, channel-flipping, fast-forwarding species. We don’t completely understand it, and we’re not altogether happy about it.”

In global health, there’s a growing tendency to demand ever-shorter summaries of complex information.
 
“Can you condense this into four pages?”

“Is there an executive summary?”

These requests, while stemming from real time constraints, reveal fundamental misunderstandings about the nature of knowledge and learning.

Worse, they contribute to perpetuating existing global health inequities.

Here is why – and a few ideas of what we can do about it.

We lose more than time in the race to brevity

The push for shortened summaries is understandable on the surface.

Some clinical researchers, for example, undeniably face increasing time pressures.

Many are swamped due to underlying structural issues, such as healthcare professional shortages.

This is the result of a significant shift over time, leaving less time for deep engagement with new information.

If we accept these changes, we lose far more than time.

Why does learning require time, depth, and context?

True understanding and the ability to apply knowledge in diverse contexts demands deep engagement, reflection, and often, struggle with our own assumptions and mental models.

Consider the process of learning a new language.

No one expects to become fluent by reading a few pages of grammar rules.

Mastery requires immersion, practice, making mistakes, and gradually building competence over time.

The same principle applies to making sense of multifaceted global health issues.

5 risks of executive summaries

Here are five risks of demanding summaries of everything:

  1. Oversimplification: Complex health challenges often cannot be adequately captured in a few pages. Crucial nuances and context-specific details get lost. Those ‘details’ may actually be the ‘how’ of what makes the difference for those leading change to achieve results.
  2. Losing context: Information that can be easily summarized (quantitative data, broad generalizations) gets prioritized over more nuanced, qualitative, or context-specific knowledge. 
  3. Stunting critical thinking: The habit of relying on summaries can atrophy our capacity for deep, critical engagement with complex ideas.
  4. Overconfidence: It assumes that learning is primarily about information transfer, rather than a process of engagement, reflection, and application. Reading a summary can give the false impression that one has grasped a topic, leading to overconfidence in decision-making.
  5. Devaluing local knowledge: Rich, contextual experiences from health workers and communities often do not lend themselves to easy summarization.

The expectation that complex local realities can always be distilled into brief summaries for consumption by decision-makers (often in the Global North) perpetuates existing power structures in global health.

The ability to demand summaries often comes from positions of power.

This can lead to privileging certain voices (those who can produce polished summaries) over others (those with deep, context-specific knowledge that resists easy summarization).

This knowledge then gets sidelined in favor of more easily digestible but potentially less relevant information.

10 ways to value and engage with knowledge in global health

Addressing the “summary culture” requires more than better time management.

It calls for a fundamental rethinking of how we value and engage with knowledge in global health.

Instead of defaulting to demands for ever-shorter summaries, we need to rethink how we engage with knowledge.

Here are 10 practical ways to do so.

  1. Prioritize productive diversity over reductive simplicity: Sometimes, it is better to engage deeply many different ideas than to seek one reductive generalization.
  2. Value local expertise: Prioritize knowledge from those closest to the issues, even when it does not fit neatly into summary format.
  3. Value diverse knowledge forms: Recognize that not all valuable knowledge can be easily summarized. Create space for stories, case studies, and rich qualitative data.
  4. Improve information design: Instead of just shortening, focus on presenting information in more accessible and engaging ways that do not sacrifice complexity.
  5. Create new formats: Develop ways of sharing information that balance accessibility with depth and nuance.
  6. Pause and reflect: What might be lost in the condensing? Are you truly seeking efficiency, or avoiding the discomfort of engaging with complex, potentially challenging ideas? Are you willing to advocate for systemic changes that truly value deep learning and diverse knowledge sources?
  7. Challenge the demand: When asked for summaries, push back (respectfully) and explain why certain information resists easy summarization.
  8. Foster critical engagement: Encourage professionals to develop skills in quickly assessing and engaging with complex information, rather than providing pre-digested summaries.
  9. Educate funders and decision-makers: Help those in power understand the value of engaging with complexity and diverse knowledge forms.
  10. Rethink the economy of time allocation: Advocate for systemic changes that value time spent on deep learning and reflection as core to effective practice and leadership.

Image: The Geneva Learning Foundation Collection © 2024

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Brevity’s burden The executive summary trap in global health
2024-07-14

Integrating community-based monitoring (CBM) into a comprehensive learning-to-action model

According to Gavi, “community-based monitoring” or “CBM” is a process where service users collect data on various aspects of health service provision to monitor program implementation, identify gaps, and collaboratively develop solutions with providers.

  • Community-based monitoring (CBM) has emerged as a promising strategy for enhancing immunization program performance and equity.
  • CBM interventions have been implemented across different settings and populations, including remote rural areas, urban poor, fragile/conflict-affected regions, and marginalized groups such as indigenous populations and people living with HIV.

By engaging service users, CBM aims to foster greater accountability and responsiveness to local needs.

  • However, realizing CBM’s potential in practice has proven challenging.
  • Without a coherent approach, CBM risks becoming just another disconnected tool.

The Geneva Learning Foundation’s innovative learning-to-action model offers a compelling framework within which CBM could be applied to immunization challenges.

The model’s comprehensive design creates an enabling environment for effectively integrating diverse monitoring data sources – and this could include community perspectives.

Health workers as trusted community advisers… and members of the community

A distinctive feature of TGLF’s model is its emphasis on health workers’ role as trusted advisors to the communities they serve.

The model recognizes that local health staff are not merely service providers, but often deeply embedded community members with intimate knowledge of local realities.

For example, in TGLF’s immunization learning initiatives, participating health workers frequently share insights into the social, cultural, and economic factors shaping vaccine hesitancy and uptake in their communities.

  • They discuss the everyday barriers families face, from misinformation to transportation challenges, and strategize context-specific outreach approaches.
  • This grounding in community realities positions health workers as vital bridges for facilitating community engagement in monitoring.

When local staff are empowered as active agents of learning and change, they can more effectively champion community participation, translating insights into tangible improvements.

Could CBM fit into a more comprehensive system from local monitoring to action?

TGLF’s model supports health workers in this bridging role by providing a comprehensive framework for local monitoring and action.

Through peer learning networks and problem-solving cycles, the model equips health staff to collect, interpret, and act on unconventional monitoring data from their communities.

For instance, in TGLF’s 2022 “Full Learning Cycle” initiative, 6,185 local health workers from 99 countries examined key immunization indicators to inform their analyses of root causes and then map out corrective actions.

  • Participants began monitoring their own local health indicators, such as vaccination coverage rates.
  • For many, this was the first time they had been prompted to use this data for problem-solving a real-world challenge they face, rather than just reporting up the next level of the health system.

They discussed many factors critical for tailoring immunization strategies.

This transition – from being passive data collectors to active data users – has proven transformative.

It positions health workers not as cogs in a reporting machine, but as empowered analysts and strategists.

By discussing real metrics with peers, participants make data actionable and contextually meaningful.

Guided by expert-designed rubrics and facilitated discussions, health workers translated this localized monitoring data into practical improvement plans.

For an epidemiologist, this represents a significant shift from traditional top-down monitoring paradigms.

By valuing and actioning local knowledge, TGLF’s model demonstrates how community insights can be systematically integrated into immunization decision-making.

However, until now, its actors have been health workers, many of them members of the communities they serve, not service users themselves.

CBM’s focus on monitoring is important – but leaves out key issues around community participation, decision-making autonomy, and strategy.

How could we integrate CBM into a transformative approach?

TGLF’s experiences suggest that CBM could be embedded within comprehensive learning-to-action systems focused on locally-led change.

TGLF’s model is more than a monitoring intervention.

  • It combines structured learning, rapid solution sharing, root cause analysis, action planning, and peer accountability to drive measurable improvements.
  • These mutually reinforcing components create an enabling environment for health workers to translate insights into impact.

In this framing, community monitoring becomes one critical input within a continuous, collaborative process of problem-solving and adaptation.

Several features of TGLF’s model illustrate how this integration could work in practice:

  1. Peer accountability structures, where health workers regularly convene to review progress, share challenges, and iterate solutions, create natural entry points for discussing and actioning community feedback.
  2. Rapid dissemination channels, like TGLF’s “Ideas Engine” for spreading promising practices across contexts, enable local innovations in response to community-identified gaps to be efficiently scaled.
  3. Emphasis on root cause analysis and systemic thinking equips health workers to interpret community insights within a broader ecosystem lens, connecting localized issues to upstream determinants.
  4. Cultivation of connected leadership empowers local actors to champion community priorities and navigate complex change processes.

TGLF’s extensive digital network connects health workers across system levels and contexts, enabling them to learn from each other’s experiences with no upper limit to the number of participants.

By contrast, CBM seems to assume that a community is limited to a physical area, which fails to recognize that problem-solving complex challenges requires expanding the range of inputs used.

Within a networked approach that connects both community members and health workers across boundaries of geography, health system level, and roles, CBM could become an integral component of a transformative approach to health system improvement – one that recognizes communities and local health workers as capable architects of context-responsive solutions.

Fundamentally, the TGLF model invites a shift in mindset about whose expertise counts in monitoring and driving system change.

CBM could provide the ‘connective tissue’ for health workers to revise how they listen and learn with the communities they serve.

For immunization programs grappling with persistent inequities, this shift from passive compliance to proactive local problem-solving is critical.

As the COVID-19 crisis has underscored, rapidly evolving public health challenges demand localized action that harnesses the full range of community expertise.

TGLF’s model offers a tested framework for actualizing this vision at scale.

By investing in local health workers’ capacity to learn, adapt, and lead change in partnership with the communities they serve, the model illuminates a promising pathway for integrating CBM into immunization monitoring and beyond.

For epidemiologists and global health practitioners, TGLF’s approach invites a reframing of how we conceptualize and operationalize community engagement in health system monitoring.

It challenges us to move beyond tokenistic participation towards genuine co-design and co-ownership of monitoring processes with local actors.

Realizing this vision will require significant shifts in mindsets, power dynamics, and resource flows.

But as TGLF’s initiatives demonstrate, when we invest in the leadership of those closest to the challenges we seek to solve, transformative possibilities emerge.

Further rigorous research comparing the impacts of different CBM integration models could help accelerate this paradigm shift, surfacing critical lessons for the immunization field and global health more broadly.

TGLF’s model not only offers compelling lessons for reimagining monitoring and improvement in immunization programs, it also provides a pathway for integrating CBM into a system that supports actual change.

CBM practitioners are likely to struggle with how to incorporate it into existing practices.

By investing in frontline health workers as change agents, and surrounding them with an empowering learning ecosystem, the model offers a path to then bring in community monitoring.

Without such leadership from health workers, it is unlikely that communities are able to participate.

The journey to authentic community engagement in health system monitoring is undoubtedly complex.

But if we are to deliver on the promise of equitable immunization for all, it is a journey we must undertake.

TGLF’s model lights one promising path forward – one that positions communities and local health workers as the beating heart of a learning health system.

While Gavi’s evidence brief affirms the promise of CBM for immunization, TGLF’s experience with its own model suggests the full potential of CBM may be realized by embedding it within more comprehensive, digitally-enabled learning systems that activate health workers as agents of change – and do so with both physical and digital communities implementing new forms of peer and community accountability that complement conventional kinds (supervision, administration, donor, etc.).

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Community-based monitoring for immunization
2024-06-30

Why asking learners what they want is a recipe for confusion

A survey of learners on a large, authoritative global health learning platform has me pondering once again the perils of relying too heavily on learner preferences when designing educational experiences.

One survey question intended to ask learners for their preferred learning method.

The list of options provided includes a range of items.

(Some would make the point that the list conflates learning resources and learning methods, but let us leave that aside for now.)

Respondents’ top choices (source) were videos, slides, and downloadable documents.

At first glance, this seems perfectly reasonable.

After all, should we not give learners what they want?

As it happens, the main resources offered by this platform are videos, slides, and other downloadable documents.

(If we asked learners who participate in our peer learning programmes for their preference, they would likely say that they prefer… peer learning.)

Beyond this availability bias, there is a more significant problem with this approach: learner preferences often have little correlation with actual learning outcomes.

And learners are especially bad at self-evaluating what learning methods and resources are most conducive to effective learning.

The scientific literature is quite clear on this point.

Bjork’s 2013 article on self-regulated learning emphatically states that: “learners are often prone to illusions of competence during learning, and these illusions can be remarkably compelling.”

The study by Deslauriers et al. (2019) provides a compelling demonstration that while students express a strong preference for traditional lectures over active learning methods, they actually learn significantly more from the active approaches they claim to dislike.

This disconnect between preference and efficacy is not surprising when we consider how learning actually works.

Effective learning requires effort, struggle, and sometimes discomfort as we grapple with new ideas and challenge our existing mental models.

It is not always an enjoyable process in the moment, even if the long-term results are deeply rewarding.

Furthermore, learners (like all of us) are subject to various cognitive biases that can lead them astray when evaluating their own learning.

The illusion of explanatory depth, for example, can cause us to overestimate how well we understand a topic after passively consuming information about it.

None of this is to say we should ignore learner perspectives entirely.

Motivation and engagement do matter for learning.

But we need to be thoughtful about how we solicit and interpret learner feedback.

Asking about preferences for specific content formats (videos, slides, etc.) tells us very little about the actual learning activities and cognitive processes involved.

A more productive approach might be to focus on understanding learners’ goals, challenges, and contexts.

What are they trying to achieve?

What obstacles do they face?

What constraints shape their learning environment?

With this information, we can design evidence-based learning experiences that truly meet their needs – even if they don’t always match their stated preferences.

As learning professionals, our job is not to give learners what they think they want.

It is to create the conditions for transformative learning experiences that expand their capabilities and perspectives.

This often means pushing learners out of their comfort zones and challenging their assumptions about how learning should look and feel.

References

Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417-444. https://doi.org/10.1146/annurev-psych-113011-143823

Deslauriers, L., McCarty, L.S., Miller, K., Callaghan, K., Kestin, G., 2019. Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences 201821936. https://doi.org/10.1073/pnas.1821936116

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Heini Utenen OpenWHO confusion about methods and learner preferences
2024-06-30

The global health community has long grappled with the challenge of providing effective, scalable training to health workers, particularly in resource-constrained settings.

In recent years, digital learning platforms have emerged as a potential solution, promising to deliver accessible, engaging, and impactful training at scale.

Imagine a digital platform intended to train health workers at scale.

Their theory of change rests on a few key assumptions:

  1. Offering simplified, mobile-friendly courses will make training more accessible to health workers.
  2. Incorporating videos and case studies will keep learners engaged.
  3. Quizzes and knowledge checks will ensure learning happens.
  4. Certificates, continuing education credits, and small incentives will motivate course completion.
  5. Growing the user base through marketing and partnerships is the path to impact.

On the surface, this seems sensible.

Mobile optimization recognizes health workers’ technological realities.

Multimedia content seems more engaging than pure text.

Assessments appear to verify learning.

Incentives promise to drive uptake.

Scale feels synonymous with success.

While well-intentioned, such a platform risks falling into the trap of a behaviorist learning agenda.

This is an approach that, despite its prevalence, is a pedagogical dead-end with limited potential for driving meaningful, sustained improvements in health worker performance and health outcomes.

It is a paradigm that views learners as passive recipients of information, where exposure equals knowledge acquisition.

It is a model that privileges standardization over personalization, content consumption over knowledge creation, and extrinsic rewards over intrinsic motivation.

It fails to account for the rich diversity of prior experiences, contexts, and challenges that health workers bring to their learning.

Most critically, it neglects the higher-order skills – the critical thinking, the adaptive expertise, the self-directed learning capacity – that are most predictive of real-world performance.

Clicking through screens of information about neonatal care, for example, is not the same as developing the situational judgment to adapt guidelines to a complex clinical scenario, nor the reflective practice to continuously improve.

Moreover, the metrics typically prioritized by behaviorist platforms – user registrations, course completions, assessment scores – are often vanity metrics.

They create an illusion of progress while obscuring the metrics that truly matter: behavior change, performance improvement, and health outcomes.

A health worker may complete a generic course on neonatal care, for example, but this does not necessarily translate into the situational judgment to adapt guidelines to complex clinical scenarios, nor the reflective practice to continuously improve.

The behaviorist paradigm’s emphasis on information transmission and standardized content may stem from an implicit assumption that health workers at the community level do not require higher-order critical thinking skills – that they simply need a predetermined set of knowledge and procedures.

This view is not only paternalistic and insulting, but it is also fundamentally misguided.

A robust body of scientific evidence on learning culture and performance demonstrates that the most effective organizations are those that foster continuous learning, critical reflection, and adaptive problem-solving at all levels.

Health workers at the frontlines face complex, unpredictable challenges that demand situational judgment, creative thinking, and the ability to learn from experience.

Failing to cultivate these capacities not only underestimates the potential of these health workers, but it also constrains the performance and resilience of health systems as a whole.

Even if such a platform achieves its growth targets, it is unlikely to realize its impact goals.

Health workers may dutifully click through courses, but genuine transformative learning remains elusive.

The alternative lies in a learning agenda grounded in advances of the last three decades learning science.

These advances remain largely unknown or ignored in global health.

This approach positions health workers as active, knowledgeable agents, rich in experience and expertise.

It designs learning experiences not merely to transmit information, but to foster critical reflection, dialogue, and problem-solving.

It replaces generic content with authentic, context-specific challenges, and isolated study with collaborative sense-making in peer networks.

It recognizes intrinsic motivation – the desire to grow, to serve, to make a difference – as the most potent driver of learning.

Here, success is measured not in superficial metrics, but in meaningful outcomes: capacity to lead change in facilities and communities that leads to tangible improvements in the quality of care.

Global health leaders faces a choice: to settle for the illusion of progress, or to invest in the deep, difficult work of authentic learning and systemic change, commensurate with the complexity and urgency of the task at hand.

Image: The Geneva Learning Foundation Collection © 2024

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2024-06-30

Many health leaders are highly analytical, adaptive learners who thrive on solving complex problems in dynamic, real-world contexts.

Their expertise is grounded in years of field experience, where they have honed their ability to rapidly generate insights, test ideas, and innovate solutions in collaboration with diverse stakeholders.

In January 2021, as countries were beginning to introduce new COVID-19 vaccines, Kate O’Brien, who leads WHO’s immunization efforts, connected global learning to local action:

“For COVID-19 vaccines […] there are just too many lessons that are being learned, especially according to different vaccine platforms, different communities of prioritization that need to be vaccinated. So [everyone]  has got to be able to scale, has got to be able to deal with complexity, has got to be able to do personal, local innovation to actually overcome the challenges.”

https://youtube.com/live/uvv-g0lXy4c

In an Insights Live session with the Geneva Learning Foundation in 2022, she made a compelling case that “the people who are working in the program at that most local level have to be able to adapt, to be agile, to innovate things that will work in that particular setting, with those leaders in the community, with those families.”

https://youtube.com/live/nCB20y49hBI

However, unlike Kate O’Brien, some senior leaders in global health disconnect their own learning practices and their assumptions about how others learn best.

When it comes to designing learning initiatives for their teams or organizations, these leaders may default to a more simplistic, behaviorist approach.

They may equate learning with the acquisition and application of specific skills or knowledge, and thus focus on creating structured, content-driven training programs.

The appeal of behaviorist platforms – with their promise of efficient, scalable delivery and easily measured outcomes – can be seductive in the resource-constrained, results-driven world of global health.

Furthermore, leaders may hold assumptions that health workers – especially those at the community level – do not require higher-order critical thinking skills, that they simply need a predetermined set of knowledge and procedures.

This view is fundamentally misguided.

A robust body of scientific evidence on learning culture and performance demonstrates that the most effective organizations are those that foster continuous learning, critical reflection, and adaptive problem-solving at all levels.

Health workers at the frontlines face complex, unpredictable challenges that demand situational judgment, creative thinking, and the ability to learn from experience.

Failing to cultivate these capacities not only underestimates the potential of these health workers, but it also constrains the performance and resilience of health systems as a whole.

The problem is that this approach fails to cultivate the very qualities that make these leaders effective learners and problem-solvers.

Behaviorist techniques, with their emphasis on passive information absorption and narrow, pre-defined outcomes, do not foster the critical thinking, creativity, and collaborative capacity needed to tackle complex health challenges.

They may produce short-term gains in narrow domains, but they cannot develop the adaptive expertise required for long-term impact in ever-shifting contexts.

To help health leaders recognize this disconnect, it is useful to engage them in reflective dialogue about their own learning processes.

By unpacking real-world examples of how they have solved thorny problems or generated novel insights, we can highlight the sophisticated cognitive strategies and collaborative dynamics at play.

We can show how they constantly question assumptions, synthesize diverse perspectives, and iterate solutions – all skills that are essential for navigating complexity, but are poorly served by rigid, content-focused training.

The goal is not to dismiss the need for foundational knowledge or skills, but rather to emphasize that in the face of evolving challenges, adaptive learning capacity is the real differentiator.

It is the ability to think critically, to imagine new possibilities, to learn from failure, and to co-create with others that drives meaningful change.

By tying this insight directly to leaders’ own experiences and values, we can inspire them to champion learning approaches that mirror the richness and dynamism of their personal growth journeys.

Ultimately, the most impactful health organizations will be those that not only equip people with essential skills, but that also nurture the underlying cognitive and collaborative capacities needed to continually learn, adapt, and innovate.

By recognizing and leveraging the powerful learning practices they themselves embody, health leaders can shape organizational cultures and strategies that truly empower people to navigate complexity and drive transformative change.

This shift requires letting go of the illusion of control and predictability that behaviorism offers, and instead embracing the messiness and uncertainty of real learning.

It means creating space for experimentation, reflection, and dialogue, and trusting in people’s inherent capacity to grow and create.

It is a challenging transition, but one that health leaders are uniquely positioned to lead – if they can bridge the gap between how they learn and how they seek to enable others’ learning.

Image: The Geneva Learning Foundation Collection © 2024

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Why health leaders who are critical thinkers choose rote learning for others-small
2024-06-30

The work by Robert A. Bjork and his colleagues is very helpful to make sense of the limitations of learners’ perceptions. Here are 8 summary points from their paper about self-regulated learning.

  1. Our complex and rapidly changing world increasingly requires self-initiated and self-managed learning, not simply during the years associated with formal schooling, but across the lifespan.
  2. Learning how to learn is, therefore, a critical survival tool, but research on learning, memory, and metacognitive processes has demonstrated that learners are prone to intuitions and beliefs about learning that can impair, rather than enhance, their effectiveness as learners.
  3. Becoming sophisticated as a learner requires not only acquiring a basic understanding of the encoding and retrieval processes that characterize the storage and subsequent access to the to-be-learned knowledge and procedures, but also knowing what learning activities and techniques support long-term retention and transfer.
  4. Managing one’s ongoing learning effectively requires accurate monitoring of the degree to which learning has been achieved, coupled with appropriate selection and control of one’s learning activities in response to that monitoring.
  5. Assessing whether learning has been achieved is difficult because conditions that enhance performance during learning can fail to support long-term retention and transfer, whereas other conditions that appear to create difficulties and slow the acquisition process can enhance long-term retention and transfer.
  6. Learners’ judgments of their own degree of learning are also influenced by subjective indices, such as the sense of fluency in perceiving or recalling to-be-learned information, but such fluency can be a product of low-level priming and other factors that are unrelated to whether learning has been achieved.
  7. Becoming maximally effective as a learner requires interpreting errors and mistakes as an essential component of effective learning rather than as a reflection of one’s inadequacies as a learner.
  8. To be maximally effective also requires an appreciation of the incredible capacity humans have to learn and avoiding the mindset that one’s learning abilities are fixed.

Source: Bjork, R.A., Dunlosky, J., Kornell, N., 2013. Self-Regulated Learning: Beliefs, Techniques, and Illusions. Annu. Rev. Psychol. 64, 417–444. https://doi.org/10.1146/annurev-psych-113011-143823

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Self-Regulated Learning: Beliefs, Techniques, and Illusions

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