Effectiveness of data augmentation to predict students at risk using deep learning algorithms

Abstract

The academic intervention to predict at-risk higher education (HE) students requires effective data model development. Such data modelling projects in the HE context may have common issues related to (a) adopting small-scale modelling that gives limited options for early intervention and (b) using imbalanced data that hinders capturing effective details of poorly performing students. We address the issues going beyond the distribution-based algorithm, using a multilayer perceptron classifier which shows better on confusion metric, recall, and precision measures for identifying at-risk students. Our proposed deep learning-based model, which uses data augmentation techniques to supplement the data instances and balance the dataset, aims to improve the prediction accuracy of whether the student will fail or not based on their interaction with the learning management systems to prevent struggling students from evasion.