Effectiveness of English Online Learning Based on Dual Channel Based Capsnet

Abstract

Web-based learning systems have quickly developed, by giving students a broader access to wide range of courses. However, when presented with a huge number of courses, it might be difficult for users to rapidly discover the ones they are interested in, from a large amount of online educational resources. As a result, a course recommendation system is crucial to increase users' learning benefit. Presently, numerous online learning platforms have developed a variety of recommender systems using conventional data mining techniques. Still, these methods have several shortcomings, like adaptability and sparsity. To solve this problem, this study provides a deep learning based English course recommendation system with the extraction of features using a dual channel based capsule network (CapsNet). This network extracts all the important features about the courses and learners and suggests suitable courses for the learners. To evaluate the proposed model’s performance, several investigations are performed on a real-world dataset (XuetangX) and outperforms existing recommendation approaches with an average of 91% precision, 45% recall, 55% f1-score, 0.798 RMSE, and 0.671 MSE. According to the experimental findings, the proposed model provides better and more reliable recommendation performance than the conventional approaches. According to the experimental findings, the proposed model provides better and more reliable recommendation performance than the conventional approaches.