Abstract:
"In the swiftly evolving landscape of online education, understanding and enhancing student
engagement has emerged as a critical challenge. However, traditional methods often fail to
provide a comprehensive understanding of student behavior. This study presents a novel
approach to monitoring student engagement by employing Deep Learning techniques,
specifically Generative Adversarial Networks (GANs) and Autoencoders, utilizing the
DAiSEE dataset.
This study used an Autoencoder model for anomaly detection to identify patterns of
disengagement. Furthermore, this utilized GANs to generate synthetic data, addressing
limitations presented by data scarcity. Despite the challenges inherent in training GANs, the
model demonstrated a promising 97% accuracy rate in identifying real instances, although its
ability to recognize fake instances necessitates further enhancements.
While these initial findings are encouraging, the research identifies several avenues for future
enhancements, including expanding data collection, incorporating additional features,
exploring other models and techniques, and fine-tuning the existing models. As such, the
findings lay a solid foundation for future exploration in the domain of online education and
deepen our understanding of the potentials of Deep Learning models in transforming this
field."
