Evaluating Splitting Approaches in the Context of Student Dropout Prediction

TL;DR

This paper investigates data splitting strategies for student dropout prediction using machine learning, demonstrating that temporal and time-based approaches outperform random splits in creating effective training and testing datasets.

Contribution

It introduces and evaluates data splitting strategies tailored for dropout prediction, highlighting the superiority of temporal and incremental history-based methods.

Findings

Random proportional splitting is unsuitable for dropout prediction.

Simple temporal splitting does not reflect real-world scenarios.

Temporal and incremental history-based splitting yield better predictive performance.

Abstract

The prediction of academic dropout, with the aim of preventing it, is one of the current challenges of higher education institutions. Machine learning techniques are a great ally in this task. However, attention is needed in the way that academic data are used by such methods, so that it reflects the reality of the prediction problem under study and allows achieving good results. In this paper, we study strategies for splitting and using academic data in order to create training and testing sets. Through a conceptual analysis and experiments with data from a public higher education institution, we show that a random proportional data splitting, and even a simple temporal splitting are not suitable for dropout prediction. The study indicates that a temporal splitting combined with a time-based selection of the students' incremental academic histories leads to the best strategy for the problem in question.