Data Augmentation with GAN to Improve the Prediction of At-Risk Students in a Virtual Learning Environment

In this paper, we explore the use of data augmentation through generative adversarial networks (GANs) for improving the performance of machine learning models in detecting at-risk students in the context of e-learning institutions. It is well known that balancing datasets can have a positive effect on improving the performance of machine learning models, especially for deep neural networks. However, undersampling can potentially result in the loss of valuable data, so data augmentation seems to be more meaningful solution when the dataset is relatively small. One of the most popular data augmentation approaches is the use of GAN networks due to their ability to generate high-quality synthetic samples that belong to the distribution of the original dataset. On the other hand, detecting at-risk students is a hot topic in learning analytics, and ability to detect these students early with high accuracy enables e-learning institutions to take necessary steps to motivate and retain students during the course. We apply this approach to the OULA dataset, a commonly used dataset in learning analytics that includes labeled at-risk students. The OULA dataset is not highly-imbalanced, making it more challenging to improve model performance through these techniques.