High-school dropout prediction using machine learning: a Danish large-scale study

TY - GEN

T1 - High-school dropout prediction using machine learning

T2 - 23rd European Symposium on Artificial Neural Networks

AU - Şara, Nicolae-Bogdan

AU - Halland, Rasmus

AU - Igel, Christian

AU - Alstrup, Stephen

N1 - Conference code: 23

PY - 2015

Y1 - 2015

N2 - Pupils not finishing their secondary education are a big societal problem. Previous studies indicate that machine learning can be used to predict high-school dropout, which allows early interventions. To the best of our knowledge, this paper presents the first large-scale study of that kind. It considers pupils that were at least six months into their Danish high-school education, with the goal to predict dropout in the subsequent three months. We combined information from the MaCom Lectio study administration system, which is used by most Danish high schools, with data from public online sources (name database, travel planner, governmental statistics). In contrast to existing studies that were based on only a few hundred students, we considered a considerably larger sample of 36299 pupils for training and 36299 for testing. We evaluated different machine learning methods. A random forest classifier achieved an accuracy of 93.47% and an area under the curve of 0.965. Given the large sample, we conclude that machine learning can be used to reliably detect high-school dropout given the information already available to many schools.

AB - Pupils not finishing their secondary education are a big societal problem. Previous studies indicate that machine learning can be used to predict high-school dropout, which allows early interventions. To the best of our knowledge, this paper presents the first large-scale study of that kind. It considers pupils that were at least six months into their Danish high-school education, with the goal to predict dropout in the subsequent three months. We combined information from the MaCom Lectio study administration system, which is used by most Danish high schools, with data from public online sources (name database, travel planner, governmental statistics). In contrast to existing studies that were based on only a few hundred students, we considered a considerably larger sample of 36299 pupils for training and 36299 for testing. We evaluated different machine learning methods. A random forest classifier achieved an accuracy of 93.47% and an area under the curve of 0.965. Given the large sample, we conclude that machine learning can be used to reliably detect high-school dropout given the information already available to many schools.

M3 - Article in proceedings

SN - 978-2-87587-014-8

SP - 319

EP - 324

BT - Proceedings. ESANN 2015

A2 - Verleysen, Michel

PB - i6doc.com

Y2 - 22 April 2015 through 24 April 2015

ER -