VIDEO: Using data to reduce dropout

South Africa simply does not have the right types of records (datasets) to measure and monitor school dropout properly. While there are several datasets tracking matric exam results, annual school surveys, and master school lists, this information is only at the ‘aggregate’ level, not at the level of individual learners. Learner-level information could help us flag which young people are most at risk of dropping out.

While collective (aggregate) data helps us understand broad trends, and draw connections and comparisons, it doesn’t help educators identify learners at risk. This is because it cannot describe the process in which learners become disengaged from school, in a way that might allow us to reach them before they drop out.

If we are serious about keeping learners in school, it’s essential that we collect and analyse data at the learner and school level. By tracking individual learners’ absenteeism, academic performance and behaviour, we can better understand their struggles and pathways through school. This will allow us to identify learners at risk of dropout, and design well-informed support programmes, as early as possible.