Rwanda has made impressive strides in access to schooling, ensuring nearly universal education coverage … virtually all children in Rwanda today have at some point been enrolled school.
The next major challenge for Rwanda’s education system is to ensure higher rates of promotion through the system and completion of basic education.
Currently Rwanda’s education system is skewed heavily towards the early years, with over 50% of all students in school stuck in primary 1 to primary 3.
In 2016, there were close to 600,000 children enrolled in primary 1, compared to less than 100,000 in primary 6.
Children enter a cycle of frequent class repetition starting in primary 1 that leads to high over-aging.
Once children are over-age, they are much more likely to drop out of school.
Two key questions for Rwanda’s education system are: (i) how to reduce repetition rates for young children; and (ii) how to reduce dropout rates for older children? Both while ensuring that children in the system receive a high quality education.
The problem is that there is currently not sufficient information on the potential drivers of repetition and dropout in Rwandan basic education and the children who are the most affected by repetition and dropout.
The solution Laterite proposed - and is currently implementing in close collaboration with the Ministry of Education and UNICEF) – is to create Rwanda’s first nationally representative study on dropout and repetition, linking for the first time in Rwanda child, parent, community and school data.
This is a major survey, in which more than 8000 children aged 6 to 18, 3000 parents, 500 communities and about 200 schools will be interviewed.
One of the major innovations of this study is that Laterite is working with children and parents to recreate children’s entire schooling trajectory, providing, for the first time, an overview of how children in Rwanda have progressed through their education and how what happens at one point in a child’s education affects their future trajectory.
Other innovations that are being implemented as part of this study include: (i) developing a system dynamics model for the Ministry of Education, to help the Ministry predict future grade-based enrolment levels and resource requirements under different scenarios; (ii) using algorithms to help the Ministry of Education transfer its school level data from thousands of excel sheets, into one comprehensive time-series dataset; and (iii) working with students from Harvard University to create a score-card based on machine learning algorithms and that teachers/schools can use to detect earlylry-on which children are the most at risk of repetition
The outcome of this effort will be concrete policy ideas on how to reduce repetition and dropout and rates and many new insights on dropout and repetition patterns.
This effort will also result in tools that the Ministry can use to better plan and target its interventions, including a system dynamics modelling tool, a scorecard system, and also more accessible school level data.
The findings will also be used in UNICEF programming to design new interventions to reduce dropout and repetition rates in basic education in Rwanda.