Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique

Most web-based educational systems contain some drawbacks, as compared to traditional classrooms. Particularly, it becomes difficult for teachers to guide students to choose an appropriate learning resource due to the large number of online learning resources. Meanwhile, student decisions make it more difficult to choose educational resources according to their circumstances. In this matter, the resource recommender system can be employed as an educational environment to recommend the educational resource advice for students, so that these recommendations can be coordinated to each student's preferences and needs. This paper presents the resource recommender system as a combination of MLP, BiLSTM, and LSTM improved deep learning networks using the attention method. Compared to similar studies conducted using DBN networks and focus only on the near past interests and preferences of users, the proposed system provides higher accuracy and more appropriate recommendations considering current interests, in addition to the user's long-term past interests. The proposed recommender system with accuracy of 0.96 and a loss of 0.0822 contains a better performance to recommend resources to students compared to other methods.

Automated summary

Compared to traditional classrooms, web-based educational systems face challenges in guiding students to choose appropriate learning resources due to the vast number of online resources available. The proposed resource recommender system integrates deep learning networks to offer more accurate and relevant recommendations based on current and long-term user interests, achieving higher accuracy and improved performance in recommending resources to students.