Getting Teachers Where They’re Needed Most: Insights from Sierra Leone’s GIS-Supported Preference Matching Algorithm

Sierra Leone faces long-standing teacher deployment challenges: too few qualified teachers in the rural and remote schools, high turnover in certain regions, subject specialist shortages, and striking underrepresentation of female teachers on payroll in remote communities. These inequalities undermine learning outcomes and widen the gap between urban and rural learners.

To address this, the Teaching Service Commission (TSC), in partnership with EdTech Hub, the Learning Generation Initiative (LGI), and Fab Inc., introduced a GIS-supported preference matching algorithm during the 2024/25 deployment cycle. Inspired by the Nobel-prize winning matching algorithm used to solve health worker allocation issues in the US & Ethiopia, the algorithm aims to place the right teachers in the right schools using transparent, data-driven criteria. Sierra Leone is among the few countries in the world to use such an innovative approach to deploy teachers to schools.

This webinar brought together policymakers, researchers, and technical experts to share what worked, what didn’t and what Sierra Leone’s experience means for other countries facing similar challenges. The webinar also brought in comparative insights from Latin America, where similar centralised teacher assignment reforms have been implemented in countries such as Ecuador, Peru and Chile.