Early dropout prediction using data mining: a case study with high school students

TLDR

Early prediction of school dropout is a serious problem, yet traditional classification methods are usually applied only at course end to maximize accuracy. The study aims to develop a methodology and algorithm for early, interpretable dropout prediction. Using data from 419 Mexican high‑school students, we performed experiments to predict dropout at different course stages, select key indicators, and compare our algorithm with classical and imbalanced classifiers. The algorithm accurately predicted dropout within the first 4–6 weeks, making it suitable for an early warning system.

Abstract

Abstract Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.