Attention and Learning Features-Enhanced Knowledge Tracing

This paper introduces ALFKT, a novel knowledge tracing model that combines attention mechanisms and learning features to create a detailed representation of students’ learning trajectories. ALFKT addresses these limitations by synergistically integrating attention mechanisms and recurrent neural networks to capture time series data efficiently. Learning gates are introduced to regulate fluctuations in the student’s knowledge state between interactions, accurately modeling the uptake rate of learning. Individual differences are accounted for by incorporating various learning characteristics, using answer accuracy to measure question difficulty and interval time to measure forgetting. These components, along with the student’s present knowledge state, enable accurate prediction of answer accuracy in subsequent interactions. Extensive experiments on four public datasets validate the superiority of ALFKT, with a remarkable 12.69% improvement in AUC over the optimal baseline model on the ASSIST2012 dataset. Additional studies authenticate the influence of each component within ALFKT. Overall, ALFKT represents a significant advancement in accurately tracing students’ knowledge states and holds promise for enhancing personalized learning experiences and contributing to educational technology.