Beyond early warning indicators : high school dropout and machine learning

This paper provides an algorithm to predict which students are going to drop out of high schools relying only on information from 9th grade. It verifies that using a parsimonious early warning system - as implemented in many schools - leads to poor results. It shows that schools can obtain more precise predictions by exploiting the available high-dimensional data jointly with machine learning tools such as Support Vector Machine, Boosted Regression and Post-LASSO. It carefully selects goodness-of-fit criteria based on the context and the underlying theoretical framework: model parameters are calibrated by taking into account policy goals and budget constraints. Finally, it uses unsupervised machine learning to divide students at risk of dropping out into different clusters