This blog has been written by Paul Atherton and Alasdair Mackintosh from Fab Inc.
Sierra Leone has made significant progress towards educational targets in recent years, but is still struggling to ensure equitable access to quality teachers for all its learners. The government is exploring innovative solutions to tackle this problem. In support of this, Fab Inc. has brought their expertise in data science and education systems, merging the two to use spatial analysis to unpack and explore this challenge.
Between 2018 and 2019, the Sierra Leone government’s Free Quality School Education (FQSE) initiative resulted in an additional 700,000 children enrolled – nearly 10% of the population. At the same time, there was a push from the Ministry and Teaching Service Commission to improve education quality and outcomes.
But, despite this recent expansion, many students in Sierra Leone still lack access to well-qualified, resourced and motivated teachers. Furthermore, disparities in pupils’ progression, completion and learning outcomes have been exacerbated by recent crises due to Ebola, economic downturn and COVID-19.
Education indicator averages do not tell the whole story about disparities in access to qualified teachers. For instance, while the national pupil-teacher ratio in Sierra Leone may seem manageable (below 40:1 in primary), the trained teacher ratio is much higher, at 58:1, and even higher outside Freetown and in remote locations. Clearly, finding ways to improve equitable deployment, or to support unqualified teachers already in these schools, is key to the country’s achievement of the UN’s Sustainable Development Goal 4 on quality education.
Figure 1: Pupil-teacher ratio for primary education by district (left); and within Kailahun district, Sierra Leone, by chiefdom (right), 2020.
In a recent article, David Moinina Sengeh, Sierra Leone’s Minister of Basic and Senior Secondary Education, highlighted how spatial analysis can be used to better understand obstacles to educational access, and thus lead to solutions and improved educational outcomes.
Spatial analysis, also referred to as geospatial analysis, is a set of techniques to explain patterns and behaviours in terms of geography and locations. It uses geographical features, such as distances, travel times and school neighbourhoods, to identify relationships and patterns.
Our team, using its expertise in both data science and education systems, examined issues linked to remoteness to produce a clearer picture of Sierra Leone’s teacher shortage. To see how the current education workforce was distributed across the country, and how well it served local populations, we drew on geo-processed population data from the Grid-3 initiative and the Government of Sierra Leone’s Education Data Hub. The project benefited from close collaboration with the Ministry and Teaching Service Commission (TSC).
Our analysis focused on teacher development, training and the deployment of new teachers across regions, drawing on exam data. Surveys of teacher training colleges (TTCs) were conducted to assess how many future teachers will need to be trained to make up for shortages. Gender and subject speciality were analysed to better address local imbalances. The team developed a matching algorithm for teacher deployment, to illustrate how schools’ needs, including aspects of qualifications and subject specialisms, can be matched to teachers’ preferences, including aspects of language and family connections, to improve allocation of both current and future teachers.
Results show the workforce is very unevenly distributed around the country, with problems in all districts. On the surface, there appears a key distinction between the Western Area (Freetown and its surrounding area) and the rest of the country. However, this is skewed by the greater prevalence of private schools in the Western Area. Looking at non-private schools only, the differences are less pronounced across districts, showing instead much greater variation within districts. For instance, in urban centres across each district, education indicators can closely resemble those within the Western Area, but outside of urban centres disparities widen dramatically. This is especially the case for access to qualified teachers and the share of female teachers and specialists in subjects such as mathematics and science.
To understand more about the urban/rural disparities, we developed a method to analyse the distance from schools to the nearest urban centre, using “as the crow flies” distance but also exploring distances along roads, which can take into account rivers and hills. Results show a drastic widening in disparities for schools that are more than 5 km (an hour’s walk) away from urban centres. It was most notable in their inability to attract qualified teachers, as these schools usually filled existing gaps with local ‘volunteer’ teachers. This was particularly the case for scarce subject specialists, who were seldom found in the most remote schools.
Based on the analysis, costed solutions were identified, building on the wider work of the Education Workforce Initiative (EWI). For example, it was possible to identify schools where subject specialists have enough time in their schedule to support other schools within walking distance that lack their specialized knowledge. Through collaborative work and shared expertise, teacher shortages can be alleviated at lower costs. In addition, there are initiatives underway, such as the Open University’s programme to support local women in remote areas to become teachers through coaching and distance learning, which can help untrained unqualified teachers to improve their skills while gaining their teaching qualifications.
Alongside current efforts to eliminate existing gaps, problems must be prevented from reoccurring. Examining the pipeline of teachers from TTCs, we note a shortage of mathematics and science specialists and a surplus of social studies teachers, both in the system and in training. Female trainees enrolled in TTCs are also scarce. At present rates of training, the supply of subject specialists will never meet the needs without intervention.
Working with the TSC, our team’s work on spatial analysis can help mitigate some of the widest disparities, while discussions as part of a new working group can continue about how to improve the long run teacher supply.
Conclusions and policy recommendations
Education systems need to strengthen both equitable deployment strategies and support to local volunteer teachers to ensure rural and remote schools have more access to trained teachers. As such we conclude and recommend the following:
- There is significant potential to incorporate spatial analysis and workforce needs (particularly for subject specialists) into system planning. The Ministry is taking this forward to develop a school infrastructure and catchment area planning policy that takes workforce needs into account.
- Improving collaboration with teacher training colleges (TTCs), and including them in wider sector discussions, can enable authorities to take a proactive approach to matching prospective teachers to local shortages.
- Teacher incentives, including non-financial options, to increase teachers’ motivation to work in remote schools can be developed, for instance including teacher preferences in deployment decisions, or giving priority for new payroll places and promotion to teachers at remote schools. Learning teams can also be explored, to make the best use of the available resources.
- Spatial analysis can be used not only for system level planning but also at local levels for such tasks as optimizing inspection routes and identifying teachers who can fill existing gaps by walking to neighbouring schools.
- Investing in data and conducting geo-mapping of schools with GIS coordinates, and making this data more open, is valuable in enabling deeper analysis and improved system planning.
The Education Workforce Initiative (EWI), funded by UK Aid, partnered with Fab Inc. and Sierra Leone’s Teaching Service Commission (TSC) to produce a series of policy papers to develop ideas to help ensure all children, especially those in poorer, more remote areas, can get access to a quality education workforce.
The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO and the International Task Force on Teachers for Education 2030 concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The ideas and opinions expressed in this publication are those of the authors; they are not necessarily those of UNESCO and do not commit the Organization.
Cover photo credit: Annie Spratt/Unsplash
Caption: School children in Sierra Leone