Enhancing E-Learning with Deep Learning: A Review of Advanced Course Recommendation Systems

In order to solve issues like information overload and personalization in online learning environments, this review looks at the use of deep learning in e-learning course recommendation systems. Research shows that the accuracy and scalability of course recommendations are greatly increased by machine learning algorithms, especially deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Sequential pattern mining (SPM) and context awareness are two methods that further improve the recommendations' personalization. Deep learning techniques, such as neural collaborative filtering (NCF) and hybrid models, have been empirically demonstrated to outperform conventional recommendation algorithms in terms of precision, recall, and other metrics. These systems are especially useful for use in MOOCs (Massive Open Online Courses) and virtual learning environments (VLEs) because they dynamically update recommendations based on new data. An important step forward in personalized e-learning has been made with the integration of these cutting-edge systems, which show promise for enhancing student engagement, retention, and overall learning outcomes.