AffectiveLearn: Emotion Enhanced Online Learning

This research introduces an approach to online learning by incorporating deep facial expression analysis to discern primary and secondary emotions, such as frustration, anxiety, boredom, and confusion. Using an advanced deep face algorithm, the system accurately detects and classifies facial expressions, providing nuanced insights into users’ emotional states during online learning. Additionally, the work explores the integration of blink rate as a supplementary metric to enhance emotion recognition accuracy. The goal is to create a responsive online learning environment by leveraging emotional cues, fostering adaptive strategies according to individual emotional states. The system features a user-friendly Graphical User Interface (GUI) and has undergone testing with an existing dataset, ensuring its efficacy. This research marks a significant step toward revolutionizing online education, offering a dynamic and emotionally intelligent learning experience for improved user engagement and educational outcomes.