In jedem vierten Land in Subsahara-Afrika erreichen über die Hälfte der Schülerinnen und Schüler am Ende der Grundschule weniger als die Mindestlesekompetenz.
100% der Kinder erreichen Mindestlesekompetenz in den unteren Klassenstufen
Learning trajectories show how many years or grades it takes for children to acquire foundational skills. Because these trajectories track the pace of learning in the system, they can help us understand how many children struggle to acquire these skills, when and how to intervene, and how different policies might impact the urgent challenge of low learning.
What can learning trajectories tell us?
Ideally, learning trajectories are steep. However, too often we see learning trajectories that are flat, indicating a slow pace of learning. A slow start can have long-term consequences. This is because when children fall behind, the curriculum often moves on to more advanced material with which they lack the prerequisite skills to engage.
The trajectories of many countries reveal that the pace of learning is far too slow, and that this is true from the very beginning of school. In Grades 1 and 2, when global goals and many national curricula assume that children are learning to read, only a small minority of children actually do so.
These skills are the basic prerequisites for future learning and are measured here by whether children could read a simple 70-word story aloud (i.e. “Manh is in class two.”) and answer five simple questions about it (i.e. “What class is Manh in?”). These foundational skills are typically below the minimum proficiency level defined by Sustainable Development Goal (SDG) global indicator 4.1.1 a. The calculated trajectories therefore overestimate progress towards SDG 4.1.1 a, but are still useful estimates to illustrate the pace of learning.
For a country that wants to address low learning, the policy takeaways are:
- Measure and prioritize foundational skills, beginning in the early grades.
- Align instruction to children’s actual pace of learning. This can be done in many ways, including by setting clear learning goals focused on foundational learning, adjusting the pace of the curriculum and supporting teachers to understand and adapt their teaching to children’s current learning levels.
Policy simulations: access and learning
We can use learning trajectories to compare the potential impact of different policies. Here, we simulate the impact of policies aimed at increasing access and enhancing learning. Access-oriented policies seek to increase the number of years children spend in school. They include policies such as free primary and secondary education, school building, automatic promotion and other policies that primarily focus on enrolment or attainment. Learning-oriented policies seek to increase how much children learn at each grade in school. Examples of these policies include setting and measuring progress against clear learning goals, realigning instruction to match children’s pace of learning, and supporting effective teaching.
Every child should have access to school, and getting children into school is a necessary first step towards universal learning. However, these simulations show that further increases in access will, on their own, do very little to address learning. In contrast, there are large potential gains to be made by increasing learning per grade to match the pace of higher-performing low- and lower-middle-income countries.
Policy simulations: equality
Learning trajectories are also a powerful tool to understand the differences in learning between groups inside countries. Here, we compare the learning of the rich and the poor, and simulate the impact of policies that try to reduce inequality between them.
Averaging across all low- and lower-middle-income countries in the data set, the children of the rich (family wealth in the top 20 per cent) go to school more, and learn more in each grade, than the children of the poor (family wealth in the bottom 20 per cent). The equal access simulation shows what might happen if instead the poor got as much schooling as the rich, while the equal learning simulation shows what might happen if instead the poor learned as much per grade as the rich. In both scenarios, and especially in the equal learning simulation, inequality would decrease.
However, even the children of the rich are not getting a very good education. Nearly half of the children of the rich are failing to acquire foundational reading skills, putting a limit on the potential impact of equity-oriented policies. Simulations of equality between boys and girls, or urban and rural children, yield even smaller gains.
For a country that wants to address low learning, the policy takeaway is that closing gaps between groups inside countries is only a part of the solution. An equally pressing challenge is to make systemic changes that improve learning for all children – rich and poor, boys and girls, in urban and rural areas – to achieve a transition from a low- to a high-performing system.
Note: In this graph, the equal access simulation line does not continue after age 11 years. This is due to missing data in the sample for the equal access simulation from ages 12 to 14 years. Any missing data elsewhere on this page are displayed the same way and are sometimes also shown as a thinner line if there are missing data between two data points.
Create your own learning trajectories and policy simulations
Below is a tool to create your own learning trajectories and run policy simulations. You can explore data from multiple countries for both foundational reading and mathematics skills.
As a reminder, reading skills are measured by whether children could read a simple 70-word story aloud and answer five simple questions about the story. Mathematics skills are measured over four domains (number identification, number discrimination, simple addition, and number patterns) using 21 simple questions. A child is considered to have mastered these basic reading and mathematics skills if they answered all questions correctly. These are typically Grade 2 or 3 level skills and are below the minimum proficiency level defined by SDG global indicator 4.1.1a.
Learning trajectories builder
Learning trajectories vary greatly across contexts. This tool allows you to build, compare and export the trajectories that are most relevant for you. You can visualize and compare the trajectories of different countries, or you can build and compare the trajectories of groups inside a country.
Learning trajectories reveal how quickly children acquire foundational learning skills. They are an important tool that can be used to raise public awareness of low learning outcomes and help policymakers plan how to take action.
The content of this webpage was created by the RISE (Research on Improving Systems of Education) Programme – the large-scale education systems research programme supported by funding from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), the Australian Government’s Department of Foreign Affairs and Trade (DFAT), and the Bill and Melinda Gates Foundation. The Programme is managed and implemented through a partnership between Oxford Policy Management and the Blavatnik School of Government at the University of Oxford.