Enhancing Learner Engagement Detection in Online Learning: A Multi-Attention Machine Learning Approach for Facial Emotion Recognition

This study aims to enhance the detection of learner engagement in online learning environments through a novel multi-attention machine learning approach for facial emotion recognition. The primary objective is to develop an advanced model that improves the accuracy, robustness, and real-time applicability of engagement detection by integrating spatial and temporal attention mechanisms. The proposed approach leverages deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to process facial emotion data captured during online learning sessions. A dataset of annotated facial images was used to train and validate the model, ensuring a comprehensive representation of emotional states. The computational complexity and efficiency of the model are analyzed to assess its feasibility for real-time applications. Comparative performance evaluations against traditional emotion recognition methods demonstrate the superiority of the multi-attention approach, yielding higher accuracy, improved precision, recall, and F1-score. The proposed framework effectively identifies subtle emotional expressions, leading to enhanced engagement detection with a significant reduction in false positives and false negatives. Additionally, practical limitations such as sensitivity to lighting conditions, partial occlusions, and computational overhead are discussed. Visual and statistical analyses further confirm the model’s effectiveness in capturing and interpreting facial emotions within online learning environments. The findings suggest that incorporating multiple attention mechanisms into facial emotion recognition models significantly enhances the detection of learner engagement, contributing to more adaptive and personalized educational experiences. Future research directions include expanding dataset diversity, integrating multimodal data sources, and optimizing real-time performance to further improve the effectiveness of AI-driven engagement monitoring in online education.