As Kaffenberger points out, early stagnation is one of the findings emerging from analysis of learning profiles. Learning profiles show that children begin to fall behind early in their schooling years.

We knew already that overall learning levels are low in developing countries. This can be inferred from the simple cross sections in internationally comparable tests like PISA and TIMSS, which show that 15 year olds in many low- and middle-income countries lag far behind their peers in the rich world. But these assessments only tell us where children’s skill levels are towards the end of the schooling cycle. A learning profile shows the relationship between the years a child, or group of children spend in school and the skills they acquire in each year.

What we learn from Kaffenberger and other RISE team members’ analysis of learning profiles is that often children’s learning progress stalls at much earlier grades. Armed with this knowledge policy makers might want to focus attention on preventing children from falling behind earlier in their schooling, where we see learning stagnation emerge. The important shift towards emphasis on early acquisition of basic skills is reflected in the World Bank’s focus on “Learning Poverty” – a new measure introduced by the World Bank in 2019 that estimates the proportion of 10 year olds in a country who can read and understand simple passages of text.

The work on learning profiles has also revealed the high degree of variation in learning profiles between countries. This underscores that the right policy approach will also vary from country to country, and country-specific information on schooling and learning outcomes is needed to inform policy decisions.

These are just a few examples of the types of insights that learning profiles can provide, and how by generating a more nuanced understanding of the learning crisis they can inform how we approach policy planning for solutions. Learning profiles can also be used to show differences in learning trajectories between groups of children – boys a girls, rich and poor, majority and minority groups. All of which could be used by policy makers to make empirically informed decisions about prioritization for learning.

Any good blog from a research organization will end with a call for more data, but the good news about learning profiles is that for many countries there is already data available for analyzing learning profiles. There are several different types of learning profiles, each generated from different types of data, and all can be used to inform policy makers prioritization.

In a new RISE Insight Note Kaffenberger develops a typology of learning profiles, describing three distinct types, some of which could be generated from data that countries already have, and others of which might require new data collection.

Typology of learning profiles

 Source: Kaffenberger 2019, “A Typology of Learning Profiles: Tools for Analysing the Dynamics of Learning”

What types of questions would you like to answer with learning profiles? Do you think you might have access to a dataset that could be used to generate learning profiles? Get in touch with us by email or twitter to share your ideas.