Deep Knowledge Tracking Based on Double Attention Mechanism and Cognitive Difficulty
Junhong Guo, Yonggang Ding, Ying Li, Lingling Zheng, Xinyue Ma, Lingyun Xiao · 2023
Knowledge tracking is a key technique of artificial intelligence-assisted education to predict learners' future answers based on their historical learning interaction data. Current studies on knowledge-tracking models are well-established, but often with limited accuracy in tracking learners' knowledge acquisition. This paper proposes a knowledge tracking model based on double attention mechanism and cognitive difficulty, to achieve a better understanding of the relationships among knowledge concepts, their influence on learners' knowledge acquisition and the impact of absolute difficulty and the relative difficulty of the exercises based on learners' cognitive levels. We designed a mechanism to explore the correlation between exercises-knowledge concepts and knowledge concepts-knowledge concepts, and dynamically analyze the cognitive difficulty of exercises. The model also introduces a multi-headed self-attentive mechanism to capture the long-term dependence of learners' knowledge acquisition. The results suggest that the model can effectively track learners' knowledge acquisition.