Enhancing Personalized Learning Experiences by Leveraging Deep Learning for Content Understanding in E-Learning Recommender Systems

This paper presents a novel approach to enhance personalized learning experiences in e-learning recommender systems by leveraging deep learning for content understanding. Personalized learning aims to tailor educational content and strategies to individual learner needs, improving engagement and learning outcomes. However, existing e-learning recommender systems often struggle to provide truly personalized recommendations due to limited content understanding. We propose using deep learning models, such as convolutional neural networks and recurrent neural networks, to extract complex features from educational content, enabling more accurate understanding and recommendation of relevant materials. Our experimental results demonstrate the effectiveness of deep learning in enhancing content understanding, leading to improved personalized learning experiences. This research contributes to advancing the field of e-learning by leveraging deep learning techniques to address the challenge of content understanding in personalized learning environments.