Year: 2026
Type: article
Abstract: Existing course recommendation models struggle to capture the dynamic evolution of learners’ interests, while traditional knowledge tracing methods miss long-term dependencies and neglect topic features and forgetting patterns in long interaction sequences. This study improves both tasks. For course recommendation, a model integrating multi-layer attention and a gated recurrent network hierarchically fuses long- and short-term interests and models their temporal evolution. For knowledge tracing, a self-attention model with temporal enhancement is proposed, incorporating a question feature fusion module and response time intervals to simulate the forgetting curve. The recommendation model achieves HR@5, HR@10, HR@20 of 0.557, 0.684, and 0.763. The knowledge tracing model attains an AUC of 0.828 and accuracy of 0.764. In real platform deployment, the click-through rate increases to 64.8% and the course completion rate to 55.3%. The knowledge mastery prediction accuracy reaches 86.1%, with the average learner score rising to 86.5, demonstrating effective modeling for personalized online education.
Source: Discover Artificial Intelligence
Authors: Jianping Qian, Hua Fan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7204086829