Making research and evaluation more visible is necessary, but it is not sufficient. If we succeed in connecting evaluation reports, research, administrative data and information about interventions, we will have access to a much richer body of evidence. But somebody still needs to make sense of it.
This requires more than technical data skills. We need enough people across the education system who can critically interpret evidence, assess the strength and limitations of different studies, recognise when findings can or cannot be compared, synthesise evidence across multiple sources, and translate that evidence into useful insights for decision-makers.
As I argued in an earlier post, the interesting work often starts after the information has been assembled. Good sensemaking requires moving between data and the real world it represents, while retaining uncertainty, context and different perspectives. (M&E Blog)
So alongside investment in better data and evidence infrastructure, there is a human-capability question:
Do we have enough people, in the right places in the system, with the skills and time to turn an increasingly rich evidence base into useful knowledge?
Possible responses: strengthening interpretation and sensemaking capacity
Improving access to research and data is only part of the solution. We also need to strengthen the people, processes and institutions that turn information into useful knowledge for decision-making. These ideas could work together as different parts of that system. Some of them are already underway and not new initiatives.
1. Create an annual “Sensing” publication for teacher development. Develop an annual synthesis, similar in concept to the South African Child Gauge, that brings together key data trends, research and evaluation findings, policy developments, emerging initiatives and evidence gaps. The New Leaders Foundation is already doing some really interesting work to explore what is happening in the system. The purpose would be to help the sector periodically stand back and ask what we are learning collectively about teacher development.
2. Connect academic research hubs into the evidence system. Build more deliberate links with universities and research centres producing relevant education research. Rather than expecting decision-makers to find and interpret academic publications themselves, relevant findings could be translated into short, accessible evidence products and incorporated into the wider evidence base. Academic partners could also contribute to periodic synthesis and sensemaking. Dr. Cally Ardington is doing some very interesting work through ADEA, RESEP is a reliable hub of interesting research outputs, and our joint data repositories are growing for example - Data First. Our colleagues at the Research Coordination, Monitoring and Evaluation (RCME) Directorate in the DBE are also very influential in this system.
3. Build data capability around real DBE decisions.
Our colleagues at the RCME are also instrumental in providing high-quality analysis and reporting to inform the broader system. But, we need more people able to use the available data in SA-SAMS/DDD to inform teacher development decisions. The Subject Advisors in Districts are potential key users. Develop customised reports using SA-SAMS, DDD and other available data around the actual decisions that officials need to make, accompanied by practical data-use bootcamps. The emphasis would be less on generic data literacy and more on helping people ask the right questions, interpret particular indicators and use evidence appropriately in their work.
4. Strengthen the analysts who support decision-makers. Develop custom reports and offer more advanced bootcamps for analysts, M&E specialists and research staff in NGOs, foundations, corporates and government. These could focus on triangulation, synthesis, interpretation, visualisation and communicating uncertainty, building a distributed network of people capable of translating complex evidence for decision-makers.
5. Provide ongoing support through an imagined Ask Ngolwazi. Training does not solve the problem when someone encounters an unfamiliar indicator or report six months later. Ask Ngolwazi, an AI-enabled data coach, could provide just-in-time support: helping users understand indicators, interrogate reports, formulate useful questions and recognise what the available data can—and cannot—tell them. It would complement human analytical capacity rather than replace it.
6. Create a mechanism for partners to contribute capacity and resources. Drawing inspiration from the Bana Pele collaborative framework, develop a mechanism through which DBE can identify priority evidence, analytical and capacity needs and invite donors, foundations, corporates, universities and other partners to contribute against them. This could help shift external investment from fragmented individual initiatives towards shared system priorities and capabilities.
Taken together, these are not simply six training or knowledge-management activities. They begin to describe an evidence-use system:
Generate evidence → make it visible → translate and synthesise it → build people's capacity to interpret it → support them when they use it → mobilise additional capacity where the system needs it.
7. Use futures scenarios to stay adaptive
We need to recognise that some of the ideas we develop today may become irrelevant surprisingly quickly because we are in a period of transition. Borrowing from futures research, we could develop a small set of plausible scenarios for the future of teacher development and use them to test our assumptions, notice emerging signals, and keep adapting our investments and approaches.
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