Dual-tower Feature Fusion for Student Ontology and Explainable Knowledge Tracing

Knowledge Tracing (KT) aims to model students' knowledge states through their learning interactions. While Deep Knowledge Tracing (DKT) leverages deep learning to capture complex learning patterns, existing methods often fail to jointly exploit structured student features and semantic relationships of textual knowledge components (KCs), limiting both performance and explainability. {\color{blue}To address these, we propose Feature Fusion of Dual-tower Knowledge Tracing (FFTKT) that consists of three key components: the student tower, the knowledge tower, and the fusion module.The student tower employs a Transformer encoder to process sequential student behavioral features. The knowledge tower, built upon a BERT-based encoder, models textual features of KCs. The Fusion Module dynamically aligns student and KCs interactions through a self-attention mechanism augmented with learnable memory tokens. Experimental results on ASSISTment2012/2017 demonstrate that FFTKT achieves superior AUC performance, outperforming DKT+ by 1.3 \(%\) to 6.7$%$. Through feature visualization, FFTKT reliably explains the correlations between student behaviors and KC mastery states.}