BackHome
Refresh: OffView Personal Schedule
Session Submission Summary
Share...

Direct link:

Learning in groups, learning in loops using data for development: Evidence-based action and implementation-informed iteration

Mon, March 24, 4:30 to 5:45pm, Palmer House, Floor: 7th Floor, Dearborn 2

Group Submission Type: Formal Panel Session

Proposal

With the growth of data for development, evidence-based interventions and reforms have increasingly applied “what we know” about what works in education (Akyeampong et al, 2023). Given more than a dozen years of investment in researching “what works,” there is ample basis for where to start in promoting learning, basic skills and the goals of SDG4. However, as an industry we engage less often in exploring how an intervention works in practice, how that evidence is used, whether and how the findings are replicated, and how the results in each context might inform or ensure scaling. Even when we do use additional rounds of data for development, we tend not to share the results of those efforts widely so that stakeholders locally or globally can learn from our process and progress (Dowd, 2024).

This panel will share four instances of implementation research. First, we’ll hear from Building Tomorrow about iterating to ensure comprehension skills in TaRL-inspired programming in Uganda. Next, Meerkat Learning will share how they are using data to compare approaches for scalable teacher coaching in rural Namibian schools. Then FHI 360 will show how it is using evidence to improve and further test teacher coaching models. Finally, Impact Network will share how evidence is informing their adaptation of an effective intervention for teaching sound-spelling relationships for use in government schools. All have chosen to be a part of uBoraBora, a fund for using implementation evidence to inform continuous improvement and become “better, better” (the meaning of uBoraBora in Kiswahili) in pursuit of children’s learning. Two organizations are engaging in evidence-informed action and two are pursuing implementation-informed iteration. We’ll explore via examples the difference between these two ways of learning in loops and the meaning of their progress to date for the ongoing improvement of their learning solutions.

This group of implementers is using data for development in a continual process of implementation research to apply what works in FLN in their contexts, adapt and improve it as they move to scale their work. Building from the BE2 guidance note on implementation research (Allison, 2023), they ask and answer questions about implementation to learn whether and how contextual factors affect successful implementation in a specific system and act upon the evidence to expand impact and scale. They actively apply the What Work Hub for Global Education framework that urges implementers to get beyond efficacy trials into “Efficacy +” studies that “pressure test evidence” in new contexts and at expanded scale (Angrist & Kaffenberger, 2024).

This panel continues the conversation about what works, why, how and for whom. All are engaged in implementation research around their FLN programming with the support of uBoraBora, aiming to get better, better. They have used evidence to determine their action and implementation options to iterate towards what works best in their contexts. They will speak to what they have found so far, how it has shaped implementation and the ways in which it has informed the pathway to scale. They’ll reflect on the systems needed to keep data for development both coming in and being used as well as the results that they have sought and the lessons that they learned along the way. Then we’ll have Clio Dintilhac of the Gates Foundation start the reflection and dialogue as a discussant.

Sub Unit

Organizer

Chair

Individual Presentations

Discussant