Statlog - Identifying students at greater risk of dropout
Societal issues
Identifying students at greater risk of dropout
Background
- All high schools seek to increase their graduation rates.
- Limited human resources and budgets require interventions to be efficiently targeted.
- A school board wanted to assess the potential of machine-learning methods for identifying students who were most likely to drop out.
- They also wanted to use econometric methods to understand what were the most important predictors of dropout.
Our approach
- We engineered more than 450 features covering sociodemographic characteristics, earning difficulties, attendance, behaviour, and academic results.
- Several machine learning models were trained and econometric models estimated.
Our results
86%
accuracy in predicting dropout among students.
These tools allowed the school board to proactively allocate resources to students with the greatest needs and risks.