GAN-Based Data Augmentation for Learning Behavior Analysis in MOOCs

The analysis of learning behavior in Massive Open Online Courses (MOOCs) holds immense potential for personalized learning and improving educational outcomes. While several MOOC datasets exist, such as KDD 2015 and Coursera, the Learning Behavior Analytics Dataset presents a unique and rich source of information, encompassing diverse aspects of learner interaction and engagement. However, the limited size of this dataset poses a significant challenge for applying deep learning techniques, which typically require large amounts of data for effective training. To address this data scarcity issue, we propose the application of Generative Adversarial Networks (GANs) for data augmentation. By learning the underlying distribution of the original dataset, GANs can generate synthetic data points that retain the characteristics and patterns of the real data. Augmenting the Learning Behavior Analytics Dataset with these synthetic samples allows for the training of more robust and generalizable deep learning models, enabling in-depth analysis of learning behaviors and unlocking new possibilities for personalized interventions and adaptive learning systems within MOOC environments.