Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing

Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning. Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing. By applying the Gated Recurrent Unit (GRU) and the Attention model, this approach designs a dynamic graph over different time steps. Through learning feature information and topology representation of nodes/learners, this model can predict with high accuracy of 80,63% learners with low knowledge acquisition and prepare them for further recommendation.