00:00:01 Introduction and recap of part one

Welcome to the second session on integrating participation into M&E systems. Today, the focus shifts from principles to practice. The session begins with a quick recap of the first part, followed by an exploration of how participation can be integrated across the entire M&E cycle, from defining outcomes to informing decisions. The discussion will go step-by-step through co-designing indicators, data collection, analysis, and using findings to inform decisions. Then, the focus will zoom out to look at the broader M&E systems, including the necessary conditions for consistent participation and how digital tools can support this. Finally, the webinar will cover key trade-offs and challenges encountered in practice.

In the first session, the focus was on why participation matters, what participatory M&E should look like, and the associated challenges. A key message was that participation is about power and decision-making, determining who defines success, interprets data, and influences decisions. It is crucial to center rights holders as decision-makers, not just as data sources, representing a fundamental shift in M&E thinking. Participation only truly matters if it influences outcomes; otherwise, it risks becoming an extractive process.

00:01:28 The participatory M&E cycle

While the first part focused on why participation matters, today's question is how to actually design for it. Participation requires intentional integration into the design of M&E systems and processes. Looking at the participatory M&E cycle, when communities are centered, the process starts with defining results, where communities determine what success looks like. Next is tracking progress, which involves community monitoring rather than just data collection. This is followed by reflecting and learning, where the community discusses what the data means. Finally, actions are adapted, ensuring that community inputs directly inform decisions and program changes.

To illustrate this, consider a rural nutrition program aiming to improve food security and child nutrition in a marginalized community. In a traditional M&E approach, indicators are defined by the NGO or donor based on standard frameworks. These are technically strong but may lack grounding in local realities. Data is typically collected through annual household surveys by external enumerators, analyzed internally by the organization, and turned into reports. This results in limited community ownership, as communities are excluded from defining success, interpreting results, or influencing decisions. Even if the data is technically sound, it may not be meaningful or actionable locally.

In contrast, a participatory approach involves the community actively across the process. When defining results, the community determines what nutrition improvement means to them. During progress tracking, community members gather data. They reflect and learn together, discussing findings in community meetings. Finally, program adjustments are made with direct community input.

00:03:54 Co-designing outcomes and indicators

The first major shift is in how success is defined. Participation starts long before data collection; it begins with defining what the program is trying to achieve. A participatory approach asks what change looks like from the community's perspective, how they define improvement, and what a better situation would look like for them. This shifts the starting point from externally defined objectives to locally grounded perspectives. It also means moving beyond purely technical or predefined indicators. While standardized indicators are useful, they do not always capture what matters most to people. The goal is to complement technical indicators by broadening the definition of meaningful data, allowing programs to capture lived experiences, priorities, and challenges that standard tools often miss.

Co-creation workshops are a practical method for defining results participatorily. While there is no rigid process, a structured approach helps move from open discussions to concrete indicators. Preparation is critical. It involves carefully selecting a diverse group of participants, including different genders, age groups, and livelihoods, rather than just local leaders. Facilitation must also be planned, considering language and inclusivity to avoid biased results.

The next step is discussing priorities, which is where participation truly begins. Instead of starting with indicators, the conversation opens up by asking about people's realities, such as what a good year looks like, when things are most difficult, and what changes would matter most to their households.

Following this, the discussion narrows to define specific outcomes, helping the group articulate what success actually looks like. For example, success might mean having enough food year-round or accessing preferred foods without relying on coping strategies.

These ideas are then translated into measurable indicators by asking how the community would know this change is happening. If the community states that things are better when they can eat preferred foods for more months of the year, the indicator becomes the "number of months households can access preferred foods." The objective is to create something meaningful and understandable, rather than technically perfect.

The final, essential step is validation. This ensures everyone understands the indicator in the same way and that it accurately reflects the community's intentions. Asking if the indicator makes sense and captures the discussion turns participation into shared ownership.

00:07:21 Participatory data collection

Once success is defined, the next question is how to track it over time. This involves shifting toward community-led data collection. Instead of relying solely on external enumerators, community members actively participate as contributors to the monitoring process, not just as respondents. Participatory approaches emphasize simplicity and regularity, relying on simple indicators tracked frequently. The goal is to collect useful data consistently rather than simply collecting more data. Additionally, participatory monitoring combines quantitative data to track trends with qualitative insights to understand the reasons behind those trends, providing a more complete picture of change.

Organizing community monitoring requires a simple, consistent, and sustainable approach. Preparation sets the foundation by identifying trusted, available community monitors who represent different groups. It is crucial to be very clear about what is being tracked. If the indicator, such as the number of months households can access preferred foods, is not clearly understood at this stage, inconsistencies will arise later.

Training is essential for ensuring consistency and confidence. The indicator must be explained in simple, practical terms, clarifying what counts as "preferred foods" and "access." Training should include role-plays and examples, such as simulating a household visit, and must cover basic ethics like confidentiality, respect, and consent.

The monitoring process itself should be light and practical. For regular, monthly tracking, short and focused data collection methods are best, such as quick household check-ins, small group discussions, or observations. Instead of long questionnaires, monitors ask a few consistent questions, prioritizing consistency over precision.

Data should be recorded simply and structured at the moment of collection, using paper-based forms or digital tools like ActivityInfo. The format must be easy to use, requiring minimal effort, such as a simple table with yes or no questions, ensuring the data can be easily aggregated and reviewed later.

Regularly reviewing the collected data is a critical step. This involves looking for inconsistencies or gaps and following up with community monitors through quick monthly check-ins, group reflection sessions, and spot-checking. Providing support ensures data quality and keeps monitors engaged, recognized, and motivated. Without ongoing support, participation tends to fade. Ultimately, participatory monitoring succeeds when it is simple, regular, and supported.

00:11:26 Participatory analysis and sense-making

After data is collected, the next step is making sense of it. A participatory approach goes beyond numbers. While data shows trends and patterns, it does not explain why they are happening. Involving the community in interpreting the data brings essential context, experience, and local knowledge. Because communities are closest to the reality behind the numbers, they can explain what the data actually reflects and help identify underlying causes and driving factors that data alone cannot reveal.

This interpretation is facilitated through sense-making workshops. Preparation involves making the data accessible by compiling and simplifying it, avoiding technical language or complex tables. For example, presenting a simple chart showing months of food security is more effective than sharing raw datasets. If people cannot understand the data, they cannot meaningfully engage with it. Facilitation must also be planned to ensure inclusion, perhaps using small group discussions so everyone has a chance to speak.

During the workshop, findings are first presented in a clear and neutral way, focusing on a few key messages and using visuals. At this stage, the focus is solely on presenting what the data shows, such as households reporting access to preferred food for only four to five months per year, without yet attempting to explain it.

The core of the process is interpretation to test hypotheses. Open questions are asked about why trends are happening, when the most difficult months occur, and what the main drivers are. Communities might point to seasonal income gaps, price increases, or agricultural cycles, providing context that varies across different groups.

Conclusions must then be validated to check if the findings reflect reality and match the community's experience, or if there are inaccuracies. This step prevents misinterpretation and builds ownership. Finally, key insights, explanations, and priorities are documented, such as noting that access is lowest between March and April due to a lack of income. It is also important to agree on what these insights mean for the program moving forward.

00:14:35 Adapting actions and joint decision-making

The final step in the cycle is determining what to do with the interpreted data. In a participatory approach, data is the starting point for conversations that lead to decisions. Participation becomes meaningful when community insights influence program adjustments and responses. The loop must be closed by sharing decisions back with the community, explaining what will change, what will not, and why. This demonstrates accountability and shows that participation leads to tangible outcomes.

Joint decision-making sessions are a practical method for this phase. Preparation involves summarizing key findings and being transparent about which decisions can actually be influenced. This step is often done internally by staff. Findings are then shared back with communities, often starting in smaller, separate group discussions to ensure diverse perspectives are heard before bringing them into a joint session to build a shared understanding.

Prioritization is conducted through structured discussions, asking what the most important issues are and what has the biggest impact on people's lives. Simple tools like voting or ranking can make this inclusive. The core joint decision-making moment involves community representatives and program staff discussing possible actions, balancing community priorities with project feasibility. While not all decisions are made here, community input must clearly influence them, often involving negotiation.

Finally, decisions are translated into action by defining responsibilities and timelines. These decisions must be communicated to the wider community, not just the participants, using accessible channels like community meetings or posters. Closing the loop ensures people understand the outcomes of their participation.

00:17:51 Embedding participation in M&E systems

Participation does not happen through isolated activities; it requires a supportive system. The M&E system itself must enable participation consistently at scale and over time. This involves several concrete elements. First, budget must be allocated for participation, including time, facilitation, and follow-up; otherwise, participation remains symbolic. Second, roles must be adapted. M&E teams need facilitation and engagement skills, moving beyond a traditional focus solely on data.

Data systems must also capture how data was generated and who was involved, making participation visible so it can be sustained and improved. Workflows need to include dedicated space and routines for reflection, discussion, and feedback, rather than just focusing on reporting cycles. Finally, the system must support downward accountability, ensuring communities receive, understand, and use the data, rather than just upward accountability to donors.

For example, tracking the community-defined indicator of food access was made participatory by the system around it. Resources were allocated for workshops and training. Roles were clearly defined, empowering community members as monitors and decision-makers. A digital system, like ActivityInfo, captured and tracked the data. Regular routines were established to review and reflect on the findings, ensuring the data was used. Ultimately, this system fed into decisions, such as adjusting food distribution, making the participation meaningful.

00:23:30 Using digital tools for participatory M&E

Tools only matter if they reflect broader choices about how systems work; a tool alone does not make a system participatory. A strong M&E system and its tools should enable intentional design for participation, support different roles, track data over time, and facilitate reflection and learning. If a tool only collects information without helping engage people or use the data, participation remains limited.

In practice, a digital platform like ActivityInfo can translate these ideas into a real system. The database starts with reference data, which is a catalog of static information used across different forms, such as lists of villages, registered households, and community monitors. It also tracks the indicators, specifically noting that they were defined by the community and collected via community-led surveys, reflecting participation in the system's design.

For data collection, community monitors can use a collection link to access forms on mobile devices, allowing them to submit data directly into the database without needing full user accounts. The forms are designed with clear, simplified questions and examples to ensure accurate data gathering. Once submitted, the data is automatically aggregated.

The platform also supports reporting and visualization. Simple reports and charts can be generated to show trends, such as a decrease in food access during specific months, disaggregated by village, gender, or age. These reports can be published and shared via a link for use in community sense-making workshops.

After workshops, facilitators can use specific forms to register the outcomes, documenting the date, participants, key findings, and community interpretations. M&E officers can then log the resulting decisions, assign action owners, and track whether feedback was shared back with the community. The system manages different roles and permissions, ensuring community monitors only access data collection forms, while facilitators and staff have appropriate access to edit records and view reports. The platform also supports feedback and complaint forms, accessible via QR codes, allowing community members to submit issues anonymously or with contact details so staff can close the feedback loop.

00:33:22 Navigating trade-offs and challenges

Building a participatory system involves navigating real tensions and making strategic design choices. First, participation takes time, which often conflicts with tight project timelines and reporting cycles. Instead of attempting participation everywhere, workflows should be designed strategically, focusing participation where it adds the most value and simplifying processes elsewhere.

Second, indicators defined by communities do not always align with donor requirements. The solution is not to choose one over the other, but to design the system to track both community-defined indicators and standard reporting indicators side-by-side.

Third, it is not realistic to involve everyone in every step, and participation is not automatically inclusive. Systems must structure participation by defining in advance who will be involved in data collection, analysis, and decision-making.

Finally, when communities contribute data, questions of ownership and access emerge. Systems need clear rules defining who has access to the data, what is shared, and what safeguards are in place. Without this clarity, participation can easily become extractive. These tensions can be managed through deliberate system design, as the level of participation ultimately depends on the choices made in building the system.

00:36:15 Key takeaways

Several key takeaways summarize the integration of participation into M&E systems. Participation is about how the entire system is designed, not just about using different tools. What is measured matters, but who gets to define it is equally important. Participation is only meaningful when it influences decisions, rather than just consulting people. Strong systems ensure data flows both upward to donors and downward back to communities. While there is no one-size-fits-all approach, intentional and inclusive design is crucial. Ultimately, participation shifts who produces knowledge and who holds the power to act on it.

00:40:22 Q&A session

During the Q&A session, several practical questions were addressed. Regarding budget allocation for M&E activities, there is no fixed recommended percentage, as it depends heavily on the project's scale, participant engagement, and the tools used. Even with a small budget, organizations can achieve a lot by using flexible tools and engaging people efficiently.

On the topic of qualitative indicators, digital systems like ActivityInfo include text fields to capture and store qualitative data. Analyzing this data can involve searching for specific keywords to identify recurring themes, though the exact method depends on the specific scenario and analytical needs. Output indicators can also be expressed as percentages, such as the percentage of households with access to preferred foods throughout the month.

When establishing participatory M&E systems within a donor landscape, acceptance often depends on how the approach is presented. Sharing tangible results, such as videos of community members explaining the program's impact, can effectively demonstrate the value of participatory methods to donors.

For communities with low digital literacy, participatory methods do not necessarily require reading or writing. Sense-making workshops can utilize small group discussions where trained staff capture the main points and enter them into the digital system. If the goal is for community members to eventually manage the database themselves, training them on the platform is valuable. However, if the focus is solely on co-designing indicators, low-tech methods like discussions are more appropriate for capturing community priorities before staff build the digital forms.

Finally, when deciding whether to grant data collectors full user access or just share a data collection link, organizations must consider data sensitivity and costs. Collection links are cost-effective for large numbers of collectors and work well offline, but the dropdown options are visible to anyone with the link. For highly sensitive data or smaller teams, providing full user accounts ensures better data protection and allows the system to automatically track exactly which user entered each record.