A Survey on Personalized Learning Course Recommendations with Bert and Collaborative Filtering

E-learning is the general term for an electronic learning environment that is based on formalized instruction and makes use of electronic resources. Whether learning happens in conventional classrooms or somewhere else entirely, integrating computers and the Internet is a keystone of this strategy. A network-enabled transfer of knowledge and skills that may be used to deliver education to a variety of audiences concurrently or at different times is what defines e-learning. E-learning was first viewed with skepticism since it was seen to lack human connection, but it has since developed, and e-learning websites are now an essential part of the learning process. Dynamic recommendation algorithms are now used by e-learning websites to improve user experience. These platforms use cutting-edge algorithms to overcome the drawbacks of conventional systems and enable more efficient learning. Using the in the current systems To overcome this, a dynamic recommendation system is suggested that combines cutting-edge methods such as BERT to better comprehend user preferences and queries, as well as recurrent neural networks (RNNs) to increase prediction performance. To improve prediction tasks, RNNs, a kind of neural network, are added to the recommendation system. The proposed e-learning system uses RNNs for dynamic sequence modeling and BERT for natural language comprehension to provide users with more effective and personalized learning experiences that dynamically adjust to their preferences. This strategy overcomes the drawbacks of earlier systems and demonstrates how e-learning technology is constantly evolving.