Summary
In Sierra Leone, teacher deployment has long been influenced by political pressure, undermining equity and education quality. To address this, EdTech Hub, in collaboration with the Teaching Service Commission and Fab Inc., introduced a data-driven algorithm that matches teachers to schools based on objective criteria like qualifications and location. This system empowers educators, fosters transparency, and ensures fair placement.
Together with Learning Generation Initiative and Fab Inc., EdTech Hub conducted qualitative research to gather process learning from the roll-out of the data-driven teacher deployment algorithm. The initiative highlights how data and collaboration can reshape public systems, reduce political influence, and promote equal opportunities in education.
Teachers who passed the exam 2,431
Teachers allocated to preferred schools 54%
Global partners involved 4
Why Teacher Deployment Matters
In many low- and middle-income countries, the recruitment and deployment of civil servants present a major policy challenge. In the education sector, governments have heavily invested in the education workforce to ensure all learners can access a quality education. Yet, schools in rural and deprived areas continue to struggle with a lack of trained and qualified staff.
Since teacher salaries constitute the costliest education inputs, developing an effective teacher workforce by prioritising the professionalisation of teachers and ensuring their effective management is a critical first step to improve education outcomes. A key part of this process is ensuring enough qualified teachers are in the right places. Even where there are enough teachers, their uneven distribution across subjects, levels, and geographic areas can exacerbate existing inequalities.
Bridging the Gap
The challenge
Teacher deployment in Sierra Leone has historically been a discretionary and politicised process. Personal connections, political influence, and favouritism are known to have informed placement decisions. District Directors or headteachers often received calls from influential individuals requesting jobs or favourable placements for specific teachers. This undermined equity, accountability, and the quality of education. There has also been the long-time challenge of retaining good and qualified teachers due to a lack of a clear and streamlined deployment mechanism. With just 40% of teachers on the government payroll, Sierra Leone’s education system relies heavily on volunteer teachers, who are disproportionately assigned to underserved rural and hard-to-reach regions. Moreover, up to a fifth of teachers do not take up their assignments in remote locations.
The Intervention
EdTech Hub, Fab Inc., and the Learning Generation Initiative (LGI) have collaborated with the Teaching Service Commission (TSC) to provide technical support for improving teacher allocation and conducting a series of studies on key elements of the deployment process, including teacher preferences, teacher mobility, and retention nationwide. Alongside the technical support, Fab Inc., supported by EdTech Hub funding, had designed an open-source teacher deployment algorithm to support governments worldwide in strengthening workforce planning processes. This enables a user to flexibly incorporate their own criteria, including both school and teacher preferences as required, and to visualise the outputs of the algorithm clearly.
The Impact
Some of the impacts and key changes observed as a result of the introduction of the algorithm include:
- Improving the quality of payroll teachers: In the last exam round, 2,431 teachers passed, and all were added to the payroll. This number continues to grow as digital support improves exam preparedness. More highly qualified teachers were selected in the 2024/25 deployment than in the existing payroll workforce, with 10% more having higher qualifications.
- Increased empowerment to resist political pressure and influence: A senior TSC officer recently reported being approached by a high-ranking government official requesting special treatment for a teacher who scored only 43% in the teacher licensing exam. Previously, such requests were difficult to refuse due to fear of political repercussions. Now, the officer confidently declined, explaining that the system is algorithm-driven and beyond personal influence. Instead, he offered the official resources to help the teacher study and retake the exam.
- Fostered equity and transparency: The algorithm allows decisions to be justified objectively. Staff no longer feel pressured to make biased placements and can point to the system as the decision-maker.
- Teacher preferences largely respected: 54% of teachers were placed in their preferred schools, especially in the earlier stages of deployment. However, 7% of teachers were allocated to a school more than 5km from their preferred choice of school.
Further Insights
General Reflections
Data can help with fighting entrenched challenges, corruption and inequality in the teaching sector. We see a very good example of how data is shifting the mindset and shaping systems to ensure that teachers are given equal opportunities based on merit that come with transparency.
This work also illustrates how systems can only work and develop if there is clear communication on the process, and the right stakeholders are involved. There have been several workshops that have helped the stakeholders not to look at this as an algorithm, but to look at it as a functional system where all stakeholders have a transparent way of working.
Setting up a successful system takes a lot of interaction between the technical layer and the government decision-making layer to configure it. Later in the process, a rule was introduced where teachers with a preference for a school of a particular religion would only be considered for deployment to a school of that same religion, to avoid deploying teachers to schools with different teaching patterns and content.
Since the algorithm relies on results for teachers who have passed their licensing examination, the TSC and Fab Inc. introduced practice tests and digital support tools to help teachers succeed in the licensing exams. Early evidence suggests that most teachers who pass the practice test go on to pass the real one.
EdTech Hub’s collaboration with the Ministry of Basic and Senior Secondary Education (MBSSE) and the Teaching Service Commission (TSC) in Sierra Leone has shown what’s possible when data, transparency, and collaboration come together. By supporting the design and evaluation of the data-driven teacher deployment system, EdTech Hub demonstrated that technology can improve equity and accountability in real time.
The pilot’s success has already informed government decisions and inspired other countries to explore similar data-led approaches. Building on this impact will require continued effort and investment.
Recommendations for Stakeholders
Sierra Leone’s experience, enabled by EdTech Hub’s research, technical support, and partnership, is proof that evidence and innovation can make government systems more equitable, efficient, and transparent. As the work is taken up by the education system, sustained investment in data infrastructure, capacity, and governance will be vital to ensure every child has access to the teachers they need to learn and thrive.
- Strengthen teacher support systems by expanding access to digital tools and practice exams to help teachers prepare for the licensing exam. Additionally, the education systems should provide ongoing professional development tailored to rural and underserved areas.
- Test varying simulations of the algorithm criteria and ensure the balance in trade-offs between different criteria settings to ensure it is achieving the intended aims before deciding upon the final selection.
- Institutionalise transparency by embedding the algorithmic deployment system into national policy frameworks and ensuring regular audits and public reporting to maintain trust and accountability.
- Promote cross-sector learning by sharing lessons with other ministries and countries facing similar deployment challenges. While at it, encourage regional collaboration to build a community of practice around data-driven governance.
- Improve planning and coordination by applying lessons learnt from this initial implementation to better coordinate activities and timelines across all stages of the next deployment cycle.
- For sustainability, monitor and evaluate impact to track long-term outcomes such as student performance, teacher retention, and community satisfaction. This data can be used to inform future education reforms and resource allocation.
Related Evidence
Impact Through Partnership
