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#learningstrategy — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #learningstrategy, aggregated by home.social.

  1. Reda Sadki @[email protected] ·

    Comparative analysis of workforce development models in the global malaria elimination agenda

    The stagnation in global malaria mortality reduction has forced a re-evaluation of the tools and strategies currently deployed in high-burden countries.

    While biological challenges such as insecticide resistance and parasite mutations are well-documented, a critical bottleneck remains the capacity of the human workforce to implement technical strategies with precision.

    The transition from control to elimination requires a fundamental shift in workforce development.

    It demands moving beyond the passive transmission of technical knowledge toward models that recognize the value of the health worker.

    People who work for health, especially those who engage directly with communities, are likely to possess unique insights into local transmission dynamics and community behavior.

    This analysis reviews four predominant capacity-building architectures currently active in the malaria landscape.

    These initiatives are assessed based on their ability to scale to the district and community levels, their cost-effectiveness, and their capacity to validate and utilize the tacit knowledge held by local staff.

    Malaria learning model 1. The academic massive open online course model

    The most prominent example of the digital transmission model is the MalariaX series offered by Harvard University.

    This initiative utilizes the Massive Open Online Course (MOOC) format to democratize access to high-level scientific knowledge.

    Strengths

    The primary strength of this model is the unparalleled quality of its technical content.

    It provides participants in low-resource settings with direct access to global experts and the latest scientific evidence regarding vector biology, epidemiology, and immunology.

    The digital format allows for infinite scalability in terms of access.

    Anyone with an internet connection can technically access the material.

    This eliminates the geographical barriers that often exclude peripheral health workers from elite training.

    Limitations

    The model suffers from the “know-do” gap.

    While it effectively transmits theoretical knowledge, it lacks a structural mechanism to ensure this knowledge is applied to local realities.

    The pedagogy relies heavily on passive consumption of video lectures which reinforces the hierarchy of “expert” versus “learner.”

    It fails to account for the specific needs of local health workers who must adapt global scientific principles to context-specific challenges, such as unexpected climate shifts or community resistance.

    The assessment mechanisms verify knowledge retention rather than the ability to navigate these local complexities.

    Consequently, it undervalues the learner’s own experience and offers no channel for the “global expert” to learn from the “local expert” who is managing the disease daily.

    Malaria learning model 2. The normative cascade training model

    The World Health Organization (WHO) and national malaria programs typically rely on the cascade model to disseminate new guidelines.

    This approach involves training a core group of master trainers at the national level who then train regional officers, who in turn train district and facility staff.

    Strengths

    This model ensures strong alignment with national policy and global normative guidance.

    It maintains a clear chain of command and reinforces the authority of the Ministry of Health.

    It is particularly effective for standardization, such as ensuring that a specific treatment protocol for severe malaria is introduced uniformly across the health system.

    Weaknesses

    The cascade model is plagued by the dilution of quality as training moves down the chain.

    Information is frequently distorted or simplified by the time it reaches the community health worker.

    Structurally, it treats the health worker as a passive vessel to be filled with instructions rather than a thinking professional who understands the local ecosystem.

    It is also prohibitively expensive and logistically heavy.

    It often relies on per diems that distort participant motivation and create a “training aristocracy” where access is determined by seniority rather than need.

    Crucially, this model often interprets local adaptation as non-compliance.

    It fails to recognize that frontline workers often deviate from protocols not out of ignorance but out of necessity, driven by supply chain ruptures or specific community demands that only they understand.

    Malaria learning model 3. The fellowship model

    Initiatives such as the African Leadership and Management Training for Impact in Malaria Eradication (ALAMIME) represent the fellowship model.

    These programs target high-potential program managers for intensive, long-term leadership development, often in partnership with universities.

    Strengths

    This model addresses the critical “soft skills” gap identified in malaria elimination policy reviews.

    It moves beyond technical biology to teach management, advocacy, and financial planning.

    By focusing on African leadership, it actively works to decolonize the expertise hierarchy and fosters strong regional ownership.

    The cohort-based approach builds deep professional bonds among future leaders of national malaria programs.

    Weaknesses

    The fundamental limitation is scalability and exclusivity.

    These programs are resource-intensive and reach a small number of individuals per year.

    While they produce high-quality leaders at the top, they cannot reach the critical mass of district and community personnel required to execute malaria strategies.

    This reinforces a top-heavy leadership structure that ignores the need for “micro-leadership” at the facility level.

    It overlooks the reality that a district nurse or community health worker must also exercise leadership and diplomacy every day to secure community trust.

    By focusing on the elite, this model inadvertently devalues the significance of the leadership required at the last mile.

    Malaria learning model 4. The field epidemiology training program model

    The Field Epidemiology Training Program (FETP) functions as a learning-by-doing apprenticeship.

    Residents work within the health system to investigate outbreaks and analyze surveillance data under the mentorship of experienced epidemiologists.

    Strengths

    This model closely aligns learning with work.

    It is an “applied” model where the output of the training is often a tangible public health product.

    It effectively builds data literacy and analytical capacity.

    It grounds the learner in the reality of the field rather than the theory of the classroom.

    Weaknesses

    Like the fellowship model, the FETP is difficult to scale due to the requirement for intense, one-on-one mentorship.

    It is a high-cost intervention per learner.

    Furthermore, the rigorous focus on surveillance and epidemiology often overshadows the operational implementation challenges faced by generalist health workers.

    While it produces excellent surveillance officers, it does not necessarily equip the broader workforce to utilize their own data for local decision-making.

    It often extracts data for central analysis rather than empowering local staff to interpret the trends they witness daily.

    This failure to devolve analytical power ignores the fact that local workers are often the first to notice anomalies, such as climate-driven shifts in vector behavior, long before they appear in national databases.

    Four recommendations to strengthen malaria learning and capacity-building

    The current landscape of malaria capacity building reveals a functional and epistemic schism.

    The academic and normative models excel at defining what needs to be done but fail to support the workforce in how to do it within their specific constraints.

    The fellowship and apprenticeship models build deep capacity but are structurally incapable of reaching the volume of workers necessary for elimination.

    A significant gap exists for a model that combines the scalability of digital platforms with the implementation rigor of the apprenticeship approach.

    To achieve malaria elimination, future initiatives need to:

    1. Move beyond knowledge verification to value validation.
    2. Recognize that local health workers are not the problem to be fixed but the owners of the solution.
    3. Utilize the existing workforce rather than parallel structures.
    4. Replace financial incentives with the professional motivation that comes from having one’s local knowledge recognized and used to solve the problems they face every day.

    References

    General context & the “know-do” gap

    Model 1: The academic MOOC model (MalariaX)

    Model 2: The normative cascade model and incentives

    Model 3: The fellowship model (ALAMIME)

    • ALAMIME. African Leadership and Management Training for Impact in Malaria Eradication. Makerere University School of Public Health.
      https://alamime.musph.ac.ug/
    • Couper, I., Ray, S., Blaauw, D., et al. (2018). Curriculum and training needs of mid-level health workers in Africa: a situational review from Kenya, Nigeria, South Africa and Uganda. BMC Health Services Research, 18(1), 553.
      https://doi.org/10.1186/s12913-018-3362-9

    Model 4: The Field Epidemiology Training Program (FETP)

    Strategic recommendations and value validation

    #globalHealth #globalMalariaEliminationAgenda #HRH #learningCulture #learningStrategy #malaria #workforceDevelopment
    #workforcedevelopment #malaria #learningstrategy #learningculture #hrh #globalmalariaeliminationagenda
  2. Reda Sadki @[email protected] ·

    Comparative analysis of workforce development models in the global malaria elimination agenda

    The stagnation in global malaria mortality reduction has forced a re-evaluation of the tools and strategies currently deployed in high-burden countries.

    While biological challenges such as insecticide resistance and parasite mutations are well-documented, a critical bottleneck remains the capacity of the human workforce to implement technical strategies with precision.

    The transition from control to elimination requires a fundamental shift in workforce development.

    It demands moving beyond the passive transmission of technical knowledge toward models that recognize the value of the health worker.

    People who work for health, especially those who engage directly with communities, are likely to possess unique insights into local transmission dynamics and community behavior.

    This analysis reviews four predominant capacity-building architectures currently active in the malaria landscape.

    These initiatives are assessed based on their ability to scale to the district and community levels, their cost-effectiveness, and their capacity to validate and utilize the tacit knowledge held by local staff.

    Malaria learning model 1. The academic massive open online course model

    The most prominent example of the digital transmission model is the MalariaX series offered by Harvard University.

    This initiative utilizes the Massive Open Online Course (MOOC) format to democratize access to high-level scientific knowledge.

    Strengths

    The primary strength of this model is the unparalleled quality of its technical content.

    It provides participants in low-resource settings with direct access to global experts and the latest scientific evidence regarding vector biology, epidemiology, and immunology.

    The digital format allows for infinite scalability in terms of access.

    Anyone with an internet connection can technically access the material.

    This eliminates the geographical barriers that often exclude peripheral health workers from elite training.

    Limitations

    The model suffers from the “know-do” gap.

    While it effectively transmits theoretical knowledge, it lacks a structural mechanism to ensure this knowledge is applied to local realities.

    The pedagogy relies heavily on passive consumption of video lectures which reinforces the hierarchy of “expert” versus “learner.”

    It fails to account for the specific needs of local health workers who must adapt global scientific principles to context-specific challenges, such as unexpected climate shifts or community resistance.

    The assessment mechanisms verify knowledge retention rather than the ability to navigate these local complexities.

    Consequently, it undervalues the learner’s own experience and offers no channel for the “global expert” to learn from the “local expert” who is managing the disease daily.

    Malaria learning model 2. The normative cascade training model

    The World Health Organization (WHO) and national malaria programs typically rely on the cascade model to disseminate new guidelines.

    This approach involves training a core group of master trainers at the national level who then train regional officers, who in turn train district and facility staff.

    Strengths

    This model ensures strong alignment with national policy and global normative guidance.

    It maintains a clear chain of command and reinforces the authority of the Ministry of Health.

    It is particularly effective for standardization, such as ensuring that a specific treatment protocol for severe malaria is introduced uniformly across the health system.

    Weaknesses

    The cascade model is plagued by the dilution of quality as training moves down the chain.

    Information is frequently distorted or simplified by the time it reaches the community health worker.

    Structurally, it treats the health worker as a passive vessel to be filled with instructions rather than a thinking professional who understands the local ecosystem.

    It is also prohibitively expensive and logistically heavy.

    It often relies on per diems that distort participant motivation and create a “training aristocracy” where access is determined by seniority rather than need.

    Crucially, this model often interprets local adaptation as non-compliance.

    It fails to recognize that frontline workers often deviate from protocols not out of ignorance but out of necessity, driven by supply chain ruptures or specific community demands that only they understand.

    Malaria learning model 3. The fellowship model

    Initiatives such as the African Leadership and Management Training for Impact in Malaria Eradication (ALAMIME) represent the fellowship model.

    These programs target high-potential program managers for intensive, long-term leadership development, often in partnership with universities.

    Strengths

    This model addresses the critical “soft skills” gap identified in malaria elimination policy reviews.

    It moves beyond technical biology to teach management, advocacy, and financial planning.

    By focusing on African leadership, it actively works to decolonize the expertise hierarchy and fosters strong regional ownership.

    The cohort-based approach builds deep professional bonds among future leaders of national malaria programs.

    Weaknesses

    The fundamental limitation is scalability and exclusivity.

    These programs are resource-intensive and reach a small number of individuals per year.

    While they produce high-quality leaders at the top, they cannot reach the critical mass of district and community personnel required to execute malaria strategies.

    This reinforces a top-heavy leadership structure that ignores the need for “micro-leadership” at the facility level.

    It overlooks the reality that a district nurse or community health worker must also exercise leadership and diplomacy every day to secure community trust.

    By focusing on the elite, this model inadvertently devalues the significance of the leadership required at the last mile.

    Malaria learning model 4. The field epidemiology training program model

    The Field Epidemiology Training Program (FETP) functions as a learning-by-doing apprenticeship.

    Residents work within the health system to investigate outbreaks and analyze surveillance data under the mentorship of experienced epidemiologists.

    Strengths

    This model closely aligns learning with work.

    It is an “applied” model where the output of the training is often a tangible public health product.

    It effectively builds data literacy and analytical capacity.

    It grounds the learner in the reality of the field rather than the theory of the classroom.

    Weaknesses

    Like the fellowship model, the FETP is difficult to scale due to the requirement for intense, one-on-one mentorship.

    It is a high-cost intervention per learner.

    Furthermore, the rigorous focus on surveillance and epidemiology often overshadows the operational implementation challenges faced by generalist health workers.

    While it produces excellent surveillance officers, it does not necessarily equip the broader workforce to utilize their own data for local decision-making.

    It often extracts data for central analysis rather than empowering local staff to interpret the trends they witness daily.

    This failure to devolve analytical power ignores the fact that local workers are often the first to notice anomalies, such as climate-driven shifts in vector behavior, long before they appear in national databases.

    Four recommendations to strengthen malaria learning and capacity-building

    The current landscape of malaria capacity building reveals a functional and epistemic schism.

    The academic and normative models excel at defining what needs to be done but fail to support the workforce in how to do it within their specific constraints.

    The fellowship and apprenticeship models build deep capacity but are structurally incapable of reaching the volume of workers necessary for elimination.

    A significant gap exists for a model that combines the scalability of digital platforms with the implementation rigor of the apprenticeship approach.

    To achieve malaria elimination, future initiatives need to:

    1. Move beyond knowledge verification to value validation.
    2. Recognize that local health workers are not the problem to be fixed but the owners of the solution.
    3. Utilize the existing workforce rather than parallel structures.
    4. Replace financial incentives with the professional motivation that comes from having one’s local knowledge recognized and used to solve the problems they face every day.

    References

    General context & the “know-do” gap

    Model 1: The academic MOOC model (MalariaX)

    Model 2: The normative cascade model and incentives

    Model 3: The fellowship model (ALAMIME)

    • ALAMIME. African Leadership and Management Training for Impact in Malaria Eradication. Makerere University School of Public Health.
      https://alamime.musph.ac.ug/
    • Couper, I., Ray, S., Blaauw, D., et al. (2018). Curriculum and training needs of mid-level health workers in Africa: a situational review from Kenya, Nigeria, South Africa and Uganda. BMC Health Services Research, 18(1), 553.
      https://doi.org/10.1186/s12913-018-3362-9

    Model 4: The Field Epidemiology Training Program (FETP)

    Strategic recommendations and value validation

    #globalHealth #globalMalariaEliminationAgenda #HRH #learningCulture #learningStrategy #malaria #workforceDevelopment
    #globalhealth #globalmalariaeliminationagenda #hrh #learningculture #learningstrategy #malaria
  3. Reda Sadki @[email protected] ·

    Rethinking human resources for malaria control and elimination in Africa

    The comprehensive policy review by Halima Mwenesi and colleagues “Rethinking human resources and capacity building needs for malaria control and elimination in Africa” argues that the stagnation in global malaria progress is fundamentally a human resources crisis rather than solely a biological or technical failure.

    The authors posit that the current workforce is insufficient in number and ill-equipped with the necessary skills to navigate the complex transition from malaria control to elimination.

    It is a critical indictment of the status quo in malaria training and offers a roadmap for structural reform.

    This article summarizes key points from the policy review and examines how The Geneva Learning Foundation’s peer learning-to-action model could be used by national programmes to transform the health workforce.

    The mismatch between training and operational needs

    The authors identify a severe imbalance in training priorities where capacity building has historically favored biomedical and basic sciences such as entomology and parasitology.

    While essential, this focus has led to a neglect of operational, translational, and implementation sciences.

    The report highlights that while the global community produces high-level scientists who understand the parasite, it fails to produce “translational scientists” who can bridge the gap between global guidelines and local realities.

    This has resulted, they argue, in a workforce lacking the practical competencies to operationalize complex elimination strategies that require precision and adaptation.

    The deficit in leadership and social sciences

    A major finding is the specific deficit in so-called “soft skills” and social sciences which are increasingly critical as programs move toward elimination.

    The authors argue that modern malaria control requires competencies in leadership, health diplomacy, anthropology, sociology, and political analysis.

    Program managers currently lack the training to navigate complex political landscapes, mobilize domestic resources, or engage effectively with communities to sustain interventions.

    The review emphasizes that understanding community behavior and social determinants is as critical as understanding vector behavior but this is rarely reflected in curricula.

    Data illiteracy and the failure of surveillance

    The paper identifies pervasive “data illiteracy” across the workforce.

    Health workers collect vast amounts of data to satisfy donor reporting requirements but often lack the skills to interpret or use it for local decision-making.

    This results in a “data-rich but information-poor” environment.

    As countries move toward elimination, the need for real-time, granular surveillance becomes paramount.

    The current workforce is unable to perform the rapid data analysis required to detect and respond to outbreaks at the sub-national level.

    Fragmentation and lack of coordination

    The review critiques the fragmentation of investments in training, capacity-building, and technical assistance driven by donor agendas.

    It notes a lack of coordination among donors and agencies which leads to a proliferation of uncoordinated short courses and workshops that do not necessarily align with national strategic plans.

    This fragmentation is exacerbated by a lack of data on the workforce itself.

    Many countries lack a central registry of malaria personnel which makes it impossible to forecast needs, plan for attrition, or manage career pathways.

    The call for structural transformation

    The authors call for a radical shift toward “South-South” collaboration where African institutions take the lead in training.

    They advocate for moving away from ad hoc workshops toward institutionalized, long-term capacity building.

    Crucially, they recommend the use of digital platforms to democratize access to knowledge for mid-level and community-based cadres who are often excluded from elite fellowships.

    How can learning science help transform malaria training investments into tangible health worker performance?

    For a global health epidemiologist accustomed to viewing disease control through the lens of biological interventions and coverage rates, the human resource crisis described by Mwenesi and colleagues represents a “delivery failure” of validated tools.

    The Geneva Learning Foundation (TGLF) learning science model functions as a structural intervention designed to repair broken delivery mechanisms in global health and humanitarian response.

    The following analysis translates the TGLF approach into terms recognizable to an epidemiologist or program manager who operates with the assumption that training is primarily about the transmission of technical knowledge.

    Moving from passive transmission to implementation fidelity

    Epidemiologists understand that a vaccine with high efficacy in a trial often has low effectiveness in the real world due to poor administration or cold chain failure.

    Similarly, Mwenesi et al. identify that technical malaria guidelines fail because the “human infrastructure” cannot implement them.

    Traditional training assumes that if you lecture health workers on a protocol, which is a transmission of information, they will execute it.

    This is a “single-loop” assumption.

    The TGLF model introduces an “implementation loop.”

    Instead of merely receiving information, learners in the TGLF network must design a micro-project to apply the new guideline in their specific district, execute it, and report back on the results using their own local data.

    This turns the workforce from passive recipients of protocols into active testers of implementation fidelity.

    It directly addresses the “translational science” gap identified in the paper by forcing the learner to translate theory into practice immediately.

    Sceptics often argue that this approach places an undue burden on an already overworked workforce.

    However, the TGLF model embeds learning into the workflow itself.

    This is not additional work but rather “learning-based work.”

    Participants do not create hypothetical projects.

    They identify a bottleneck they are currently facing, such as a specific pocket of malaria transmission, and use the learning cycle to address it.

    This transforms the training from an external interruption into an operational support mechanism.

    By embedding learning into the workflow, it operationalizes Mwenesi’s call for translational science.

    It considers the daily struggle of the health worker as a form of structured scientific inquiry: they hypothesize a solution, test it, and report the results.

    This is implementation as science.

    Operationalizing data use for local decision-making

    Mwenesi notes that health workers collect data but do not use it.

    In the TGLF model, data is not something sent “up” to the ministry.

    It is the raw material for peer support and feedback.

    In a TGLF peer learning exercise, a district medical officer in Ghana shares their case management data to compare performance with a peer in Uganda.

    They share because they want to, not because they are required to.

    This creates a social incentive to understand and analyze one’s own data.

    It builds the “data literacy” the authors call for not through abstract statistics courses but through the practical necessity of explaining one’s own performance to a colleague.

    This process transforms data from a compliance burden into a tool for local problem-solving.

    Is there a risk that peer learning will pool ignorance?

    Is there a valid concern regarding the risk of “pooled ignorance” where peers might reinforce incorrect practices?

    The TGLF model mitigates this through “structured emergence.”

    The model does not dismiss expert knowledge but uses global guidelines as the “anchor” for local problem-solving.

    In this system, a health worker cannot simply state an opinion.

    They must submit an action plan that is peer-reviewed against a rubric derived from WHO guidelines.

    This process ensures fidelity to technical standards while allowing for necessary local adaptation.

    The aggregation of thousands of these peer-reviewed plans creates a new form of rigorous, practice-based evidence that complements expert guidance.

    Scaling “soft skills” through structured peer review

    The review calls for leadership and diplomacy skills but notes these are hard to teach in workshops.

    The TGLF model builds these skills implicitly through its pedagogical structure.

    When a participant submits an action plan, they must receive and respond to critical feedback from peers in other countries.

    They must negotiate differing viewpoints and defend their technical choices.

    This mimics the “health diplomacy” and leadership dynamics required in real-world program management.

    Furthermore, because they must engage community stakeholders to implement their projects, they practice the anthropological and social engagement skills Mwenesi identifies as missing.

    They learn leadership not by studying a theory of leadership but by leading a change initiative in their facility.

    While some experts argue that soft skills require “hard contact” in physical spaces, TGLF results suggest that physical proximity often limits a worker to their known environment and existing biases.

    The TGLF model introduces a form of “cosmopolitan localism.”

    When a nurse in rural Nigeria must explain her challenge to a peer in urban India, she is forced to articulate her context with a clarity and diplomacy not required when speaking to a neighbor.

    This defiance of distance fosters a quantum leap in communication capabilities.

    Participants report that the skills learned in negotiating these digital, cross-cultural peer relationships directly translate to better engagement with their physical-world colleagues and community leaders.

    Addressing the incentive structure and correcting expertise asymmetry

    The paper critiques the “brain drain” and the reliance on experts from the Global North.

    TGLF operationalizes the “South-South” collaboration recommended by the authors by creating a flat digital hierarchy.

    In this model, the “expert” is not a visiting consultant from Geneva but a peer who has successfully solved the problem in their own context.

    A nurse in Nigeria learns how to improve bed net usage from a nurse in Kenya who solved that exact refusal issue last month.

    This actually results in greater interest, comprehension, and use of official guidelines.

    It also validates local knowledge and creates the “critical mass of thinking professionals” that Mwenesi argues is essential for elimination.

    It shifts the source of authority from external experts to the collective intelligence of the network.

    Transforming the economy of per diem

    A common critique of moving away from face-to-face training is the reliance of health workers on per diems for financial survival.

    Mwenesi implies that the current system is unsustainable.

    The TGLF model operates on the evidence that per diem-driven training often restricts access to a “training aristocracy” of recurrent participants while excluding the frontline workers who most need the knowledge.

    TGLF replaces the financial incentive with a professional survival incentive.

    In the Nigeria Immunization Collaborative, over 4,300 health workers participated without per diems.

    They did so because the program addressed the specific pain points of their daily work.

    This filters the workforce for “positive deviants,” or those with high intrinsic motivation who are most likely to drive elimination efforts, rather than those primarily motivated by daily subsistence allowances.

    A “surveillance system” for human resources and performance

    Finally, the review notes the lack of registries and data on the workforce itself.

    The TGLF digital network acts as a real-time sensor of workforce capacity.

    By engaging thousands of health workers simultaneously, the platform generates data on who is active, what problems they are facing, and where their skills are deficient.

    For an epidemiologist, this is equivalent to a surveillance system for human resources.

    It provides the visibility needed to forecast gaps and target interventions precisely, replacing the “blind” proliferation of uncoordinated workshops with a data-driven approach to capacity building.

    Regarding concerns that digital platforms fail in low-resource settings due to poor connectivity, TGLF utilizes a “cognitively quiet” design that functions on low-bandwidth connections and mobile devices.

    This design respects the technological reality of the African context.

    Data from the Teach to Reach program, which has engaged over 60,000 participants in remote, ongoing peer learning activities , demonstrates that when the technology is adapted to the user rather than the other way around, participation rates exceed those of physical workshops.

    This scale allows for the identification of systemic patterns and workforce gaps that would be invisible in a smaller, face-to-face cohort.

    Reference

    Mwenesi, H., Mbogo, C., Casamitjana, N., Castro, M.C., Itoe, M.A., Okonofua, F., Tanner, M., 2022. Rethinking human resources and capacity building needs for malaria control and elimination in Africa. PLOS Glob Public Health 2, e0000210. https://doi.org/10.1371/journal.pgph.0000210

    Reda Sadki (2023). How do we reframe health performance management within complex adaptive systems?. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/mx5qr-qet97

    Reda Sadki (2024). Prioritizing the health and care workforce shortage: protect, invest, together. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/zzqr4-9g482

    Reda Sadki (2024). Protect, invest, together: strengthening health workforce through new learning models. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/g24b4-7fj64

    Reda Sadki (2024). What is double-loop learning in global health?. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/s4xtw-b7274

    Reda Sadki (2024). World Malaria Day 2024: We need new ways to support health workers leading change with local communities. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/yrn1r-hpz62

    #brainDrain #cosmopolitanLocalism #dataQualityAndUse #doubleLoopLearning #HalimaMwenesi #healthWorkerMotivation #healthWorkerPerformance #healthWorkforce #HRH #implementationScience #leadership #learningStrategy #learningBasedWork #localization #malaria #peerLearning #performance #softSkills #TeachToReach #translationalScience
    #translationalscience #teachtoreach #softskills #performance #peerlearning #malaria
  4. Reda Sadki @[email protected] ·

    Rethinking human resources for malaria control and elimination in Africa

    The comprehensive policy review by Halima Mwenesi and colleagues “Rethinking human resources and capacity building needs for malaria control and elimination in Africa” argues that the stagnation in global malaria progress is fundamentally a human resources crisis rather than solely a biological or technical failure.

    The authors posit that the current workforce is insufficient in number and ill-equipped with the necessary skills to navigate the complex transition from malaria control to elimination.

    It is a critical indictment of the status quo in malaria training and offers a roadmap for structural reform.

    This article summarizes key points from the policy review and examines how The Geneva Learning Foundation’s peer learning-to-action model could be used by national programmes to transform the health workforce.

    The mismatch between training and operational needs

    The authors identify a severe imbalance in training priorities where capacity building has historically favored biomedical and basic sciences such as entomology and parasitology.

    While essential, this focus has led to a neglect of operational, translational, and implementation sciences.

    The report highlights that while the global community produces high-level scientists who understand the parasite, it fails to produce “translational scientists” who can bridge the gap between global guidelines and local realities.

    This has resulted, they argue, in a workforce lacking the practical competencies to operationalize complex elimination strategies that require precision and adaptation.

    The deficit in leadership and social sciences

    A major finding is the specific deficit in so-called “soft skills” and social sciences which are increasingly critical as programs move toward elimination.

    The authors argue that modern malaria control requires competencies in leadership, health diplomacy, anthropology, sociology, and political analysis.

    Program managers currently lack the training to navigate complex political landscapes, mobilize domestic resources, or engage effectively with communities to sustain interventions.

    The review emphasizes that understanding community behavior and social determinants is as critical as understanding vector behavior but this is rarely reflected in curricula.

    Data illiteracy and the failure of surveillance

    The paper identifies pervasive “data illiteracy” across the workforce.

    Health workers collect vast amounts of data to satisfy donor reporting requirements but often lack the skills to interpret or use it for local decision-making.

    This results in a “data-rich but information-poor” environment.

    As countries move toward elimination, the need for real-time, granular surveillance becomes paramount.

    The current workforce is unable to perform the rapid data analysis required to detect and respond to outbreaks at the sub-national level.

    Fragmentation and lack of coordination

    The review critiques the fragmentation of investments in training, capacity-building, and technical assistance driven by donor agendas.

    It notes a lack of coordination among donors and agencies which leads to a proliferation of uncoordinated short courses and workshops that do not necessarily align with national strategic plans.

    This fragmentation is exacerbated by a lack of data on the workforce itself.

    Many countries lack a central registry of malaria personnel which makes it impossible to forecast needs, plan for attrition, or manage career pathways.

    The call for structural transformation

    The authors call for a radical shift toward “South-South” collaboration where African institutions take the lead in training.

    They advocate for moving away from ad hoc workshops toward institutionalized, long-term capacity building.

    Crucially, they recommend the use of digital platforms to democratize access to knowledge for mid-level and community-based cadres who are often excluded from elite fellowships.

    How can learning science help transform malaria training investments into tangible health worker performance?

    For a global health epidemiologist accustomed to viewing disease control through the lens of biological interventions and coverage rates, the human resource crisis described by Mwenesi and colleagues represents a “delivery failure” of validated tools.

    The Geneva Learning Foundation (TGLF) learning science model functions as a structural intervention designed to repair broken delivery mechanisms in global health and humanitarian response.

    The following analysis translates the TGLF approach into terms recognizable to an epidemiologist or program manager who operates with the assumption that training is primarily about the transmission of technical knowledge.

    Moving from passive transmission to implementation fidelity

    Epidemiologists understand that a vaccine with high efficacy in a trial often has low effectiveness in the real world due to poor administration or cold chain failure.

    Similarly, Mwenesi et al. identify that technical malaria guidelines fail because the “human infrastructure” cannot implement them.

    Traditional training assumes that if you lecture health workers on a protocol, which is a transmission of information, they will execute it.

    This is a “single-loop” assumption.

    The TGLF model introduces an “implementation loop.”

    Instead of merely receiving information, learners in the TGLF network must design a micro-project to apply the new guideline in their specific district, execute it, and report back on the results using their own local data.

    This turns the workforce from passive recipients of protocols into active testers of implementation fidelity.

    It directly addresses the “translational science” gap identified in the paper by forcing the learner to translate theory into practice immediately.

    Sceptics often argue that this approach places an undue burden on an already overworked workforce.

    However, the TGLF model embeds learning into the workflow itself.

    This is not additional work but rather “learning-based work.”

    Participants do not create hypothetical projects.

    They identify a bottleneck they are currently facing, such as a specific pocket of malaria transmission, and use the learning cycle to address it.

    This transforms the training from an external interruption into an operational support mechanism.

    By embedding learning into the workflow, it operationalizes Mwenesi’s call for translational science.

    It considers the daily struggle of the health worker as a form of structured scientific inquiry: they hypothesize a solution, test it, and report the results.

    This is implementation as science.

    Operationalizing data use for local decision-making

    Mwenesi notes that health workers collect data but do not use it.

    In the TGLF model, data is not something sent “up” to the ministry.

    It is the raw material for peer support and feedback.

    In a TGLF peer learning exercise, a district medical officer in Ghana shares their case management data to compare performance with a peer in Uganda.

    They share because they want to, not because they are required to.

    This creates a social incentive to understand and analyze one’s own data.

    It builds the “data literacy” the authors call for not through abstract statistics courses but through the practical necessity of explaining one’s own performance to a colleague.

    This process transforms data from a compliance burden into a tool for local problem-solving.

    Is there a risk that peer learning will pool ignorance?

    Is there a valid concern regarding the risk of “pooled ignorance” where peers might reinforce incorrect practices?

    The TGLF model mitigates this through “structured emergence.”

    The model does not dismiss expert knowledge but uses global guidelines as the “anchor” for local problem-solving.

    In this system, a health worker cannot simply state an opinion.

    They must submit an action plan that is peer-reviewed against a rubric derived from WHO guidelines.

    This process ensures fidelity to technical standards while allowing for necessary local adaptation.

    The aggregation of thousands of these peer-reviewed plans creates a new form of rigorous, practice-based evidence that complements expert guidance.

    Scaling “soft skills” through structured peer review

    The review calls for leadership and diplomacy skills but notes these are hard to teach in workshops.

    The TGLF model builds these skills implicitly through its pedagogical structure.

    When a participant submits an action plan, they must receive and respond to critical feedback from peers in other countries.

    They must negotiate differing viewpoints and defend their technical choices.

    This mimics the “health diplomacy” and leadership dynamics required in real-world program management.

    Furthermore, because they must engage community stakeholders to implement their projects, they practice the anthropological and social engagement skills Mwenesi identifies as missing.

    They learn leadership not by studying a theory of leadership but by leading a change initiative in their facility.

    While some experts argue that soft skills require “hard contact” in physical spaces, TGLF results suggest that physical proximity often limits a worker to their known environment and existing biases.

    The TGLF model introduces a form of “cosmopolitan localism.”

    When a nurse in rural Nigeria must explain her challenge to a peer in urban India, she is forced to articulate her context with a clarity and diplomacy not required when speaking to a neighbor.

    This defiance of distance fosters a quantum leap in communication capabilities.

    Participants report that the skills learned in negotiating these digital, cross-cultural peer relationships directly translate to better engagement with their physical-world colleagues and community leaders.

    Addressing the incentive structure and correcting expertise asymmetry

    The paper critiques the “brain drain” and the reliance on experts from the Global North.

    TGLF operationalizes the “South-South” collaboration recommended by the authors by creating a flat digital hierarchy.

    In this model, the “expert” is not a visiting consultant from Geneva but a peer who has successfully solved the problem in their own context.

    A nurse in Nigeria learns how to improve bed net usage from a nurse in Kenya who solved that exact refusal issue last month.

    This actually results in greater interest, comprehension, and use of official guidelines.

    It also validates local knowledge and creates the “critical mass of thinking professionals” that Mwenesi argues is essential for elimination.

    It shifts the source of authority from external experts to the collective intelligence of the network.

    Transforming the economy of per diem

    A common critique of moving away from face-to-face training is the reliance of health workers on per diems for financial survival.

    Mwenesi implies that the current system is unsustainable.

    The TGLF model operates on the evidence that per diem-driven training often restricts access to a “training aristocracy” of recurrent participants while excluding the frontline workers who most need the knowledge.

    TGLF replaces the financial incentive with a professional survival incentive.

    In the Nigeria Immunization Collaborative, over 4,300 health workers participated without per diems.

    They did so because the program addressed the specific pain points of their daily work.

    This filters the workforce for “positive deviants,” or those with high intrinsic motivation who are most likely to drive elimination efforts, rather than those primarily motivated by daily subsistence allowances.

    A “surveillance system” for human resources and performance

    Finally, the review notes the lack of registries and data on the workforce itself.

    The TGLF digital network acts as a real-time sensor of workforce capacity.

    By engaging thousands of health workers simultaneously, the platform generates data on who is active, what problems they are facing, and where their skills are deficient.

    For an epidemiologist, this is equivalent to a surveillance system for human resources.

    It provides the visibility needed to forecast gaps and target interventions precisely, replacing the “blind” proliferation of uncoordinated workshops with a data-driven approach to capacity building.

    Regarding concerns that digital platforms fail in low-resource settings due to poor connectivity, TGLF utilizes a “cognitively quiet” design that functions on low-bandwidth connections and mobile devices.

    This design respects the technological reality of the African context.

    Data from the Teach to Reach program, which has engaged over 60,000 participants in remote, ongoing peer learning activities , demonstrates that when the technology is adapted to the user rather than the other way around, participation rates exceed those of physical workshops.

    This scale allows for the identification of systemic patterns and workforce gaps that would be invisible in a smaller, face-to-face cohort.

    Reference

    Mwenesi, H., Mbogo, C., Casamitjana, N., Castro, M.C., Itoe, M.A., Okonofua, F., Tanner, M., 2022. Rethinking human resources and capacity building needs for malaria control and elimination in Africa. PLOS Glob Public Health 2, e0000210. https://doi.org/10.1371/journal.pgph.0000210

    Reda Sadki (2023). How do we reframe health performance management within complex adaptive systems?. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/mx5qr-qet97

    Reda Sadki (2024). Prioritizing the health and care workforce shortage: protect, invest, together. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/zzqr4-9g482

    Reda Sadki (2024). Protect, invest, together: strengthening health workforce through new learning models. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/g24b4-7fj64

    Reda Sadki (2024). What is double-loop learning in global health?. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/s4xtw-b7274

    Reda Sadki (2024). World Malaria Day 2024: We need new ways to support health workers leading change with local communities. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/yrn1r-hpz62

    #brainDrain #cosmopolitanLocalism #dataQualityAndUse #doubleLoopLearning #HalimaMwenesi #healthWorkerMotivation #healthWorkerPerformance #healthWorkforce #HRH #implementationScience #leadership #learningStrategy #learningBasedWork #localization #malaria #peerLearning #performance #softSkills #TeachToReach #translationalScience
    #braindrain #cosmopolitanlocalism #dataqualityanduse #doublelooplearning #halimamwenesi #healthworkermotivation
  5. Reda Sadki @[email protected] ·

    5 surprising insights from the science of successful learning

    The work of Reda Sadki offers a provocative, often counter-intuitive critique of how we learn, lead, and solve complex problems.

    Here are five surprising insights from his body of work.

    1. Text is superior to video for learning

    In an era where educational technology is obsessed with video content, immersive simulations, and flashy multimedia, Sadki argues for the humble written word.

    He asserts that the push for multimedia is often a “deception” that confuses engagement with entertainment.

    In Richard Mayer’s research on multimedia for learning actually proves text works better, Sadki re-examines the foundational science of instructional design.

    He points out that multimedia often creates “cognitive waste” by forcing the brain to split attention between visual and auditory streams.

    He argues that well-structured text is “cognitively quiet” and far better suited for the high-level critical thinking required in complex fields.

    He doubles down on this in Against chocolate-covered broccoli: text-based alternatives to expensive multimedia content.

    Here, he describes multimedia as an economic dead end.

    He argues that text is not only cheaper and easier to update but also creates a more equitable learning environment for professionals in low-bandwidth settings.

    2. Gamification is a “disaster” for humanitarian learning

    While many organizations rush to “gamify” learning with badges, points, and leaderboards, Sadki calls this trend a “dead end.”

    He argues that gamification is simply “lipstick on the pig of behaviorism,” a discredited theory that treats learners like rats in a maze responding to stimuli.

    In Why gamification is a disaster for humanitarian learning, he makes a blistering case that games fail to model the complexity of the real world.

    He points out that the dominant culture of video games often relies on violence and competition, which are antithetical to humanitarian values.

    He argues that professionals facing life-and-death decisions need critical reasoning skills, not the artificial dopamine hits of a game.

    3. Low completion rates can be a sign of success, not failure

    In the world of online courses, a low completion rate is usually seen as a failure of design.

    Sadki flips this metric on its head.

    He suggests that in professional settings, “completion” is a vanity metric, part of the legacy of education systems that kept learners in closed environments.

    In Online learning completion rates in context: Rethinking success in digital learning networks, he argues that busy professionals often engage with learning to solve a specific problem.

    Once they find the solution, they leave.

    This “drop-off” is actually efficient learning in action.

    He warns that optimizing for completion often leads to dumbing down content rather than increasing its impact.

    4. The “transparency paradox”: health workers are using AI in secret

    One of Sadki’s most startling recent observations comes from his work with frontline health workers.

    He reveals that professionals in the Global South are already using advanced Artificial Intelligence (AI) tools, but they are forced to hide this fact.

    In Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis, he describes a “transparency paradox.”

    Global health systems are often punitive.

    If a health worker admits to using AI to help draft a report or analyze data, their work is devalued as “inauthentic,” even if the quality is higher.

    This forces innovation underground and prevents organizations from learning how to effectively partner with AI.

    He expands on the solution in A global health framework for Artificial Intelligence as co-worker to support networked learning and local action, arguing that we must legitimize AI as a “co-worker” rather than a cheat.

    5. Cascade training is mathematically doomed to fail

    Finally, Sadki uses simple mathematics to dismantle one of the most common methods of training in the world: the “cascade” model, where experts train trainers, who train others.

    In Why does cascade training fail?, he demonstrates that information loss at every level of the cascade is inevitable.

    He argues that this model persists not because it works, but because it is convenient for hierarchical organizations.

    He offers a stark alternative in Calculating the relative effectiveness of expert coaching, peer learning, and cascade training, where he proves that peer learning networks are the only model capable of scaling without losing quality.

    #ArtificialIntelligence #completionRates #gamification #globalHealth #learningStrategy #multimediaLearning #RichardMayer #TheGenevaLearningFoundation
    #thegenevalearningfoundation #richardmayer #multimedialearning #learningstrategy #globalhealth #gamification
  6. Reda Sadki @[email protected] ·

    5 surprising insights from the science of successful learning

    The work of Reda Sadki offers a provocative, often counter-intuitive critique of how we learn, lead, and solve complex problems.

    Here are five surprising insights from his body of work.

    1. Text is superior to video for learning

    In an era where educational technology is obsessed with video content, immersive simulations, and flashy multimedia, Sadki argues for the humble written word.

    He asserts that the push for multimedia is often a “deception” that confuses engagement with entertainment.

    In Richard Mayer’s research on multimedia for learning actually proves text works better, Sadki re-examines the foundational science of instructional design.

    He points out that multimedia often creates “cognitive waste” by forcing the brain to split attention between visual and auditory streams.

    He argues that well-structured text is “cognitively quiet” and far better suited for the high-level critical thinking required in complex fields.

    He doubles down on this in Against chocolate-covered broccoli: text-based alternatives to expensive multimedia content.

    Here, he describes multimedia as an economic dead end.

    He argues that text is not only cheaper and easier to update but also creates a more equitable learning environment for professionals in low-bandwidth settings.

    2. Gamification is a “disaster” for humanitarian learning

    While many organizations rush to “gamify” learning with badges, points, and leaderboards, Sadki calls this trend a “dead end.”

    He argues that gamification is simply “lipstick on the pig of behaviorism,” a discredited theory that treats learners like rats in a maze responding to stimuli.

    In Why gamification is a disaster for humanitarian learning, he makes a blistering case that games fail to model the complexity of the real world.

    He points out that the dominant culture of video games often relies on violence and competition, which are antithetical to humanitarian values.

    He argues that professionals facing life-and-death decisions need critical reasoning skills, not the artificial dopamine hits of a game.

    3. Low completion rates can be a sign of success, not failure

    In the world of online courses, a low completion rate is usually seen as a failure of design.

    Sadki flips this metric on its head.

    He suggests that in professional settings, “completion” is a vanity metric, part of the legacy of education systems that kept learners in closed environments.

    In Online learning completion rates in context: Rethinking success in digital learning networks, he argues that busy professionals often engage with learning to solve a specific problem.

    Once they find the solution, they leave.

    This “drop-off” is actually efficient learning in action.

    He warns that optimizing for completion often leads to dumbing down content rather than increasing its impact.

    4. The “transparency paradox”: health workers are using AI in secret

    One of Sadki’s most startling recent observations comes from his work with frontline health workers.

    He reveals that professionals in the Global South are already using advanced Artificial Intelligence (AI) tools, but they are forced to hide this fact.

    In Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis, he describes a “transparency paradox.”

    Global health systems are often punitive.

    If a health worker admits to using AI to help draft a report or analyze data, their work is devalued as “inauthentic,” even if the quality is higher.

    This forces innovation underground and prevents organizations from learning how to effectively partner with AI.

    He expands on the solution in A global health framework for Artificial Intelligence as co-worker to support networked learning and local action, arguing that we must legitimize AI as a “co-worker” rather than a cheat.

    5. Cascade training is mathematically doomed to fail

    Finally, Sadki uses simple mathematics to dismantle one of the most common methods of training in the world: the “cascade” model, where experts train trainers, who train others.

    In Why does cascade training fail?, he demonstrates that information loss at every level of the cascade is inevitable.

    He argues that this model persists not because it works, but because it is convenient for hierarchical organizations.

    He offers a stark alternative in Calculating the relative effectiveness of expert coaching, peer learning, and cascade training, where he proves that peer learning networks are the only model capable of scaling without losing quality.

    #ArtificialIntelligence #completionRates #gamification #globalHealth #learningStrategy #multimediaLearning #RichardMayer #TheGenevaLearningFoundation
    #artificialintelligence #completionrates #gamification #globalhealth #learningstrategy #multimedialearning
  7. Reda Sadki @[email protected] ·

    5 reasons why our current systems of learning are broken – and how to fix them

    Reda Sadki’s writing explores how systems of learning matter when tackling complex challenges across global health, humanitarian aid, and education.

    Over twelve years of articles on his blog, he has built a cohesive argument for why our current systems of learning are broken and how we might fix them.

    Since 2016, his work at The Geneva Learning Foundation has demonstrated how to turn such rethinking into new ways to learn and lead in the face of critical threats to our societies.

    Here are five themes that define his work.

    1. The failure of traditional systems of learning and the peer learning alternative

    One of Sadki’s most persistent arguments is that the humanitarian and global health sectors are addicted to ineffective models of training.

    He questions the “workshop culture” that flies experts around the world at great cost with little measurable impact.

    He argues that this “sage on the stage” model assumes knowledge flows only one way: from the expert to the ignorant practitioner.

    He is equally critical of digital replacements that merely replicate this dynamic.

    In Why gamification is a disaster for humanitarian learning, he warns that dressing up behaviorist drills with points and badges does not foster the critical thinking needed in crisis zones.

    He expands on this in Experience and blended learning: two heads of the humanitarian training chimera, arguing that “transmissive” learning fails to prepare professionals for volatility and complexity.

    Instead, Sadki advocates for peer learning networks where practitioners teach and learn from each other.

    As he explains in What learning science underpins peer learning for Global Health?, the goal is not to transmit information but to foster the “co-creation” of new knowledge that is directly applicable to local contexts.

    2. Epistemic justice: valuing communities as systems of learning

    Sadki frequently uses the philosophy of Donald Schön to distinguish between the “high ground” of theory and the “swampy lowlands” of practice.

    He argues that global health suffers from “epistemic injustice” – a systematic devaluation of the experiential knowledge held by local health workers.

    In Knowing-in-action: Bridging the theory-practice divide in global health, he makes the case that the gap between global guidelines and local reality can only be bridged by recognizing frontline workers as knowledge creators, not just recipients.

    He challenges the hierarchy that dismisses local insights as mere “anecdote.”

    In Anecdote or lived experience: reimagining knowledge for climate-resilient health systems, he proposes a new framework where the collective stories of thousands of health workers shape a new, rigorous form of evidence.

    In Critical evidence gaps in the Lancet Countdown on health and climate change, he points out that the most rigorous science can miss the vital signals that only those working in communities can see.

    3. Artificial intelligence as a co-worker

    While many in education view Artificial Intelligence (AI) as a threat to integrity or a tool for cheating, Sadki frames it as a transformative partner.

    He argues that we are entering a new epoch where AI will not just be a tool we use, but a “co-worker” we collaborate with.

    In A global health framework for Artificial Intelligence as co-worker to support networked learning and local action, he outlines how AI can support the “human” parts of learning – such as feedback and synthesis – without replacing human agency.

    He explores the profound shifts in how we will interact with technology in The agentic AI revolution: what does it mean for workforce development?, describing a future where “AI agents” handle coordination, freeing humans to focus on judgment and ethics.

    He pushes this further in Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production, suggesting that AI will fundamentally change how we consume information, moving us away from linear formats like video lectures toward dynamic, interactive knowledge creation and retrieval.

    4. Learning culture as the driver of learning systems

    Sadki insists that learning is not an event but a culture.

    Drawing heavily on the research of Karen E. Watkins and Victoria Marsick, he argues that an organization’s “learning culture” is the single best predictor of its ability to adapt and perform.

    In Learning culture: the missing link in global health between learning and performance, he explains that without a culture that supports inquiry, dialogue, and risk-taking, even the best training programs will fail.

    He identifies specific weaknesses in current systems, noting in Why lack of continuous learning is the Achilles heel of immunization that health systems often prioritize task completion over the continuous learning necessary to improve those tasks.

    This theme connects deeply to leadership.

    He argues in What is the relationship between leadership and performance? that true leadership is not about authority but about fostering an environment where learning can happen at every level of the hierarchy.

    5. New ways to bridge the gap from policy to action

    Finally, Sadki focuses relentlessly on the “know-do” gap, the disconnect between global policy and local implementation.

    He argues that guidelines often fail because they are designed without the input of those who must implement them.

    In Why guidelines fail: on consequences of the false dichotomy between global and local knowledge in health systems, he dissects how the separation of “thinkers” (global experts) and “doers” (local staff) dooms many initiatives.

    He offers concrete examples of how to close this gap, such as in The Nigeria Immunization Collaborative: Early learning from a novel sector-wide approach model for zero-dose challenges, where thousands of health workers used peer learning to identify root causes of vaccine inequity that central planners had missed.

    This theme emphasizes that the solution is not more “technical assistance” from the outside, but better mechanisms to unlock the problem-solving capacity that already exists within communities.

    Beyond learning: a new operating system in global development

    Taken together, these themes provide the specifications for a new operating system in global development, one that moves beyond the limitations of the models of today.

    • Sadki’s work challenges the sector to recognize its most undervalued asset: the collective intelligence of the health and humanitarian workforce.
    • By dismantling the barriers between the “high ground” of policy and the “swampy lowlands” of practice, his framework constructs a learning ecosystem where artificial intelligence amplifies human connection and local insights continuously refine global strategy.
    • This evolution—from episodic workshops to continuous, networked problem-solving—offers a pragmatic path to close the persistent gap between investment and outcome.

    In a resource-constrained world, unlocking this latent capacity is not merely an ethical choice, but a strategic imperative to build systems resilient enough for an unpredictable future.

    #blendedLearning #epistemicJustice #learning #learningStrategy #peerLearning #workshopCulture
    #workshopculture #peerlearning #learningstrategy #learning #epistemicjustice #blendedlearning
  8. Reda Sadki @[email protected] ·

    5 reasons why our current systems of learning are broken – and how to fix them

    Reda Sadki’s writing explores how systems of learning matter when tackling complex challenges across global health, humanitarian aid, and education.

    Over twelve years of articles on his blog, he has built a cohesive argument for why our current systems of learning are broken and how we might fix them.

    Since 2016, his work at The Geneva Learning Foundation has demonstrated how to turn such rethinking into new ways to learn and lead in the face of critical threats to our societies.

    Here are five themes that define his work.

    1. The failure of traditional systems of learning and the peer learning alternative

    One of Sadki’s most persistent arguments is that the humanitarian and global health sectors are addicted to ineffective models of training.

    He questions the “workshop culture” that flies experts around the world at great cost with little measurable impact.

    He argues that this “sage on the stage” model assumes knowledge flows only one way: from the expert to the ignorant practitioner.

    He is equally critical of digital replacements that merely replicate this dynamic.

    In Why gamification is a disaster for humanitarian learning, he warns that dressing up behaviorist drills with points and badges does not foster the critical thinking needed in crisis zones.

    He expands on this in Experience and blended learning: two heads of the humanitarian training chimera, arguing that “transmissive” learning fails to prepare professionals for volatility and complexity.

    Instead, Sadki advocates for peer learning networks where practitioners teach and learn from each other.

    As he explains in What learning science underpins peer learning for Global Health?, the goal is not to transmit information but to foster the “co-creation” of new knowledge that is directly applicable to local contexts.

    2. Epistemic justice: valuing communities as systems of learning

    Sadki frequently uses the philosophy of Donald Schön to distinguish between the “high ground” of theory and the “swampy lowlands” of practice.

    He argues that global health suffers from “epistemic injustice” – a systematic devaluation of the experiential knowledge held by local health workers.

    In Knowing-in-action: Bridging the theory-practice divide in global health, he makes the case that the gap between global guidelines and local reality can only be bridged by recognizing frontline workers as knowledge creators, not just recipients.

    He challenges the hierarchy that dismisses local insights as mere “anecdote.”

    In Anecdote or lived experience: reimagining knowledge for climate-resilient health systems, he proposes a new framework where the collective stories of thousands of health workers shape a new, rigorous form of evidence.

    In Critical evidence gaps in the Lancet Countdown on health and climate change, he points out that the most rigorous science can miss the vital signals that only those working in communities can see.

    3. Artificial intelligence as a co-worker

    While many in education view Artificial Intelligence (AI) as a threat to integrity or a tool for cheating, Sadki frames it as a transformative partner.

    He argues that we are entering a new epoch where AI will not just be a tool we use, but a “co-worker” we collaborate with.

    In A global health framework for Artificial Intelligence as co-worker to support networked learning and local action, he outlines how AI can support the “human” parts of learning – such as feedback and synthesis – without replacing human agency.

    He explores the profound shifts in how we will interact with technology in The agentic AI revolution: what does it mean for workforce development?, describing a future where “AI agents” handle coordination, freeing humans to focus on judgment and ethics.

    He pushes this further in Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production, suggesting that AI will fundamentally change how we consume information, moving us away from linear formats like video lectures toward dynamic, interactive knowledge creation and retrieval.

    4. Learning culture as the driver of learning systems

    Sadki insists that learning is not an event but a culture.

    Drawing heavily on the research of Karen E. Watkins and Victoria Marsick, he argues that an organization’s “learning culture” is the single best predictor of its ability to adapt and perform.

    In Learning culture: the missing link in global health between learning and performance, he explains that without a culture that supports inquiry, dialogue, and risk-taking, even the best training programs will fail.

    He identifies specific weaknesses in current systems, noting in Why lack of continuous learning is the Achilles heel of immunization that health systems often prioritize task completion over the continuous learning necessary to improve those tasks.

    This theme connects deeply to leadership.

    He argues in What is the relationship between leadership and performance? that true leadership is not about authority but about fostering an environment where learning can happen at every level of the hierarchy.

    5. New ways to bridge the gap from policy to action

    Finally, Sadki focuses relentlessly on the “know-do” gap, the disconnect between global policy and local implementation.

    He argues that guidelines often fail because they are designed without the input of those who must implement them.

    In Why guidelines fail: on consequences of the false dichotomy between global and local knowledge in health systems, he dissects how the separation of “thinkers” (global experts) and “doers” (local staff) dooms many initiatives.

    He offers concrete examples of how to close this gap, such as in The Nigeria Immunization Collaborative: Early learning from a novel sector-wide approach model for zero-dose challenges, where thousands of health workers used peer learning to identify root causes of vaccine inequity that central planners had missed.

    This theme emphasizes that the solution is not more “technical assistance” from the outside, but better mechanisms to unlock the problem-solving capacity that already exists within communities.

    Beyond learning: a new operating system in global development

    Taken together, these themes provide the specifications for a new operating system in global development, one that moves beyond the limitations of the models of today.

    • Sadki’s work challenges the sector to recognize its most undervalued asset: the collective intelligence of the health and humanitarian workforce.
    • By dismantling the barriers between the “high ground” of policy and the “swampy lowlands” of practice, his framework constructs a learning ecosystem where artificial intelligence amplifies human connection and local insights continuously refine global strategy.
    • This evolution—from episodic workshops to continuous, networked problem-solving—offers a pragmatic path to close the persistent gap between investment and outcome.

    In a resource-constrained world, unlocking this latent capacity is not merely an ethical choice, but a strategic imperative to build systems resilient enough for an unpredictable future.

    #blendedLearning #epistemicJustice #learning #learningStrategy #peerLearning #workshopCulture
    #blendedlearning #epistemicjustice #learning #learningstrategy #peerlearning #workshopculture
  9. CTNET @CTNET ·

    It's live! 🎉 My latest article, "Note-Taking vs. Personal Knowledge Management: Why Your Brain Will Thank You," is now published.

    Dive into the fundamental differences between basic note-taking and a robust PKM system. Discover why adopting a PKM is crucial for your cognitive abilities, particularly as AI advances.

    Read it here: ctnet.co.uk/note-taking-vs-per

    #personalknowledgemanagement #pkm #notetaking #knowledgeworker #learningstrategy #ai
  10. Reda Sadki @[email protected] ·

    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

    #networkedlearning #learningstrategy #globalhealth
  11. Reda Sadki @[email protected] ·

    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

    #globalhealth #learningstrategy #networkedlearning
  12. Reda Sadki @[email protected] ·

    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 complex problems, you need to be able to:

    • Think about all the puzzle pieces and how they fit, even when you do not 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

    1. Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.
    2. Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.
    3. 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
    4. Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.

    #complexLearning #complexProblems #learningStrategy #pedagogy #wickedProblems

    #wickedproblems #pedagogy #learningstrategy #complexproblems #complexlearning
  13. Reda Sadki @[email protected] ·

    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 complex problems, you need to be able to:

    • Think about all the puzzle pieces and how they fit, even when you do not 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

    1. Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.
    2. Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.
    3. 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
    4. Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.

    #complexLearning #complexProblems #learningStrategy #pedagogy #wickedProblems

    #complexlearning #complexproblems #learningstrategy #pedagogy #wickedproblems
  14. Reda Sadki @[email protected] ·

    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

    Share this:

    #continuousLearning #experientialLearning #fieldBasdLearning #healthWorkers #learningCulture #learningStrategy #methodology #pedagogy #peerLearning #TeachToReach #TeachToReachQuestions #TheGenevaLearningFoundation

    #thegenevalearningfoundation #teachtoreachquestions #teachtoreach #peerlearning #pedagogy #methodology
  15. Reda Sadki @[email protected] ·

    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

    Share this:

    #continuousLearning #experientialLearning #fieldBasdLearning #healthWorkers #learningCulture #learningStrategy #methodology #pedagogy #peerLearning #TeachToReach #TeachToReachQuestions #TheGenevaLearningFoundation

    #continuouslearning #experientiallearning #fieldbasdlearning #healthworkers #learningculture #learningstrategy
  16. Reda Sadki @[email protected] ·

    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

    Share this:

    https://redasadki.me/2024/10/30/what-is-the-pedagogy-of-teach-to-reach/

    #continuousLearning #globalHealth #learningCulture #learningStrategy #learningTheory #pedagogicalPatterns #peerLearning #TeachToReach #TheGenevaLearningFoundation

    #thegenevalearningfoundation #teachtoreach #peerlearning #pedagogicalpatterns #learningtheory #learningstrategy
  17. Reda Sadki @[email protected] ·

    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

    Share this:

    https://redasadki.me/2024/10/30/what-is-the-pedagogy-of-teach-to-reach/

    #continuousLearning #globalHealth #learningCulture #learningStrategy #learningTheory #pedagogicalPatterns #peerLearning #TeachToReach #TheGenevaLearningFoundation

    #continuouslearning #globalhealth #learningculture #learningstrategy #learningtheory #pedagogicalpatterns
  18. Reda Sadki @[email protected] ·

    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.

    The drive for 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 in global health. 

    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

    https://redasadki.me/2024/08/27/brevitys-burden-the-executive-summary-trap-in-global-health/

    #decolonization #globalHealth #JamesGleick #learningCulture #learningStrategy #natureOfKnowledge

    #decolonization #globalhealth #jamesgleick #learningculture #learningstrategy #natureofknowledge
  19. Reda Sadki @[email protected] ·

    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

    Share this:

    https://redasadki.me/2024/08/27/brevitys-burden-the-executive-summary-trap-in-global-health/

    #decolonization #globalHealth #JamesGleick #learningCulture #learningStrategy #natureOfKnowledge

    #natureofknowledge #learningstrategy #learningculture #jamesgleick #globalhealth #decolonization
  20. Reda Sadki @[email protected] ·

    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

    Share this:

    https://redasadki.me/2024/08/27/brevitys-burden-the-executive-summary-trap-in-global-health/

    #decolonization #globalHealth #JamesGleick #learningCulture #learningStrategy #natureOfKnowledge

    #decolonization #globalhealth #jamesgleick #learningculture #learningstrategy #natureofknowledge
  21. Reda Sadki @[email protected] ·

    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.).

    Share this:

    #communityBasedMonitoring #continuousLearning #globalHealth #healthWorkers #HRH #HumanResourcesForHealth #immunization #ImmunizationAgenda2030 #learningStrategy #TheGenevaLearningFoundation #zeroDoseChildren #ZeroDoseLearningHubZDLH_

    #communitybasedmonitoring #continuouslearning #globalhealth #healthworkers #hrh #humanresourcesforhealth
  22. Reda Sadki @[email protected] ·

    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.

    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

    https://redasadki.me/2024/06/30/why-asking-learners-what-they-want-is-a-recipe-for-confusion/

    #globalHealth #learningMethods #learningStrategy #learningStyles

    #globalhealth #learningmethods #learningstrategy #learningstyles
  23. Reda Sadki @[email protected] ·

    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.

    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

    https://redasadki.me/2024/06/30/why-asking-learners-what-they-want-is-a-recipe-for-confusion/

    #globalHealth #learningMethods #learningStrategy #learningStyles

    #globalhealth #learningmethods #learningstrategy #learningstyles
  24. Reda Sadki @[email protected] ·

    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.

    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

    https://redasadki.me/2024/06/30/why-asking-learners-what-they-want-is-a-recipe-for-confusion/

    #globalHealth #learningMethods #learningStrategy #learningStyles

    #globalhealth #learningmethods #learningstrategy #learningstyles
  25. Reda Sadki @[email protected] ·

    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

    Share this:

    #globalHealth #learningMethods #learningStrategy #learningStyles

    #learningstyles #learningstrategy #learningmethods #globalhealth
  26. Reda Sadki @[email protected] ·

    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

    Share this:

    #globalHealth #learningMethods #learningStrategy #learningStyles

    #globalhealth #learningmethods #learningstrategy #learningstyles
  27. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/learn-health-but-beware-of-the-behaviorist-trap/

    #behaviorism #eLearning #healthTraining #HealthLearn #HRH #HumanResourcesForHealth #learningCulture #learningStrategy #workforceDevelopment

    #behaviorism #elearning #healthtraining #healthlearn #hrh #humanresourcesforhealth
  28. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/learn-health-but-beware-of-the-behaviorist-trap/

    #behaviorism #eLearning #healthTraining #HealthLearn #HRH #HumanResourcesForHealth #learningCulture #learningStrategy #workforceDevelopment

    #behaviorism #elearning #healthtraining #healthlearn #hrh #humanresourcesforhealth
  29. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/learn-health-but-beware-of-the-behaviorist-trap/

    #behaviorism #eLearning #healthTraining #HealthLearn #HRH #HumanResourcesForHealth #learningCulture #learningStrategy #workforceDevelopment

    #behaviorism #elearning #healthtraining #healthlearn #hrh #humanresourcesforhealth
  30. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/learn-health-but-beware-of-the-behaviorist-trap/

    #behaviorism #eLearning #healthTraining #HealthLearn #HRH #HumanResourcesForHealth #learningCulture #learningStrategy #workforceDevelopment

    #workforcedevelopment #learningstrategy #learningculture #humanresourcesforhealth #hrh #healthlearn
  31. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/learn-health-but-beware-of-the-behaviorist-trap/

    #behaviorism #eLearning #healthTraining #HealthLearn #HRH #HumanResourcesForHealth #learningCulture #learningStrategy #workforceDevelopment

    #behaviorism #elearning #healthtraining #healthlearn #hrh #humanresourcesforhealth
  32. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/why-health-leaders-who-are-critical-thinkers-choose-rote-learning-for-others/

    #adaptiveLearning #coCreation #criticalThinking #healthLearning #immunization #ImmunizationAgenda2030 #KateOBrien #leadership #learningCulture #learningStrategy #peerLearning

    #adaptivelearning #cocreation #criticalthinking #healthlearning #immunization #immunizationagenda2030
  33. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/why-health-leaders-who-are-critical-thinkers-choose-rote-learning-for-others/

    #adaptiveLearning #coCreation #criticalThinking #healthLearning #immunization #ImmunizationAgenda2030 #KateOBrien #leadership #learningCulture #learningStrategy #peerLearning

    #adaptivelearning #cocreation #criticalthinking #healthlearning #immunization #immunizationagenda2030
  34. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/why-health-leaders-who-are-critical-thinkers-choose-rote-learning-for-others/

    #adaptiveLearning #coCreation #criticalThinking #healthLearning #immunization #ImmunizationAgenda2030 #KateOBrien #leadership #learningCulture #learningStrategy #peerLearning

    #adaptivelearning #cocreation #criticalthinking #healthlearning #immunization #immunizationagenda2030
  35. Reda Sadki @[email protected] ·

    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

    https://redasadki.me/2024/06/30/why-health-leaders-who-are-critical-thinkers-choose-rote-learning-for-others/

    #adaptiveLearning #coCreation #criticalThinking #healthLearning #immunization #ImmunizationAgenda2030 #KateOBrien #leadership #learningCulture #learningStrategy #peerLearning

    #peerlearning #learningstrategy #learningculture #leadership #kateobrien #immunizationagenda2030
  36. Reda Sadki @[email protected] ·

    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

    Share this:

    https://redasadki.me/2024/06/30/why-health-leaders-who-are-critical-thinkers-choose-rote-learning-for-others/

    #adaptiveLearning #coCreation #criticalThinking #healthLearning #immunization #ImmunizationAgenda2030 #KateOBrien #leadership #learningCulture #learningStrategy #peerLearning

    #adaptivelearning #cocreation #criticalthinking #healthlearning #immunization #immunizationagenda2030
  37. Reda Sadki @[email protected] ·

    Self-regulated learning: 8 things we know about learning across the lifespan in a complex world

    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, self-managed, and self-regulated 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 self-regulated 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.

    Reference:

    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

    #learningStrategy #lifelongLearning #memory #metacognition #retrieval #selfManagedLearning #selfRegulatedLearning #transfer

    #transfer #selfregulatedlearning #selfmanagedlearning #retrieval #metacognition #memory
  38. Reda Sadki @[email protected] ·

    Self-regulated learning: 8 things we know about learning across the lifespan in a complex world

    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, self-managed, and self-regulated 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 self-regulated 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.

    Reference:

    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

    #learningStrategy #lifelongLearning #memory #metacognition #retrieval #selfManagedLearning #selfRegulatedLearning #transfer

    #learningstrategy #lifelonglearning #memory #metacognition #retrieval #selfmanagedlearning
  39. BC Team @[email protected] ·

    teamleadership.ie/post/unlocki

    #innovation #startups #learninginprogress #learningculture #learningstrategy #leadership #coaching

    #innovation #startups #learninginprogress #learningculture #learningstrategy #leadership
  40. BC Team @[email protected] ·

    teamleadership.ie/post/unlocki

    #innovation #startups #learninginprogress #learningculture #learningstrategy #leadership #coaching

    #coaching #leadership #learningstrategy #learningculture #learninginprogress #startups
  41. BC Team @[email protected] ·

    teamleadership.ie/post/unlocki

    #innovation #startups #learninginprogress #learningculture #learningstrategy #leadership #coaching

    #innovation #startups #learninginprogress #learningculture #learningstrategy #leadership
  42. Reda Sadki @[email protected] ·

    What is a system?

    Donella H. Meadows wrote the following simple, eloquent description of what is a system:

    “A system isn’t just any old collection of things.

    A system must consist of three kinds of things: elements, interconnections, and a function or purpose.

    A system is an interconnected set of elements that is coherently organized in a way that achieves something.

    The behavior of a system cannot be known just by knowing the elements of which the system is made.

    A system is more than the sum of its parts.

    It may exhibit adaptive, dynamic, goal-seeking, self-preserving, and sometimes evolutionary behavior.

    It is easier to learn about a system’s elements than about its interconnections.

    If information-based relationships are hard to see, functions or purposes are even harder.

    A system’s function or purpose is not necessarily spoken, written, or expressed explicitly, except through the operation of the system.

    Purposes are deduced from behavior, not from rhetoric or stated goals.

    The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.

    To ask whether elements, interconnections, or purposes are most important in a system is to ask an unsystemic question.

    All are essential.

    All interact.

    All have their roles.

    But the least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.”

    Understanding what is a system is the starting point to tackling complex problems.

    Meadows, Donella H., 2008.Thinking in systems: A primer. Chelsea Green Publishing.

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    #complexity #DonellaHMeadows #learningStrategy #systemsTheory

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