Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings

Excerpt

arXiv:2508.17092v3 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content. Many KT models rely on knowledge concepts (KCs), which represent the skills required for each item. However, some of these models are vulnerable to label leakage, a phenomenon in which the input data inadvertently reveal the correct answer, particularly in datasets with multiple KCs per question. We propose