Detecting Learning Patterns and Student Engagement in Online Courses Using Deep Learning

This study introduces LearnTrans, a novel model architecture that integrates the Transformer architecture with attention mechanisms to discern learning patterns and gauge student engagement in online courses. LearnTrans employs Transformer encoder layers with self-attention mechanisms to capture dependencies within the sequential interactions of students with course content. Through rigorous experimentation on a diverse dataset collected from prominent online learning platforms, including Coursera, Udemy, and edX, LearnTrans demonstrates significant performance improvements over baseline methods. Specifically, the model achieves an average accuracy increase of 33% in learning pattern detection and 29% in student engagement prediction tasks. These findings underscore the efficacy of the proposed LearnTrans model in capturing intricate patterns and dependencies within online learning data, offering promising avenues for enhancing educational outcomes in digital learning environments.