arXiv:2603. 02830v2 Announce Type: replace-cross Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions.
By Prarthana Bhattacharyya, Joshua Mitton, Ralph Abboud, Simon Woodhead
The paper introduces TIZSKT, a zero‑shot cross‑domain knowledge tracing model that separates behavioral sequence modeling from domain‑local historical evidence construction. It uses text as an index via a frozen text encoder and unsupervised per‑domain projection to build question addresses, then aggregates a student’s past outcomes into a question‑specific historical correctness summary. Experiments on three public datasets show that TIZSKT outperforms baselines in mean AUC and accuracy, with ablations confirming the importance of text addressing and cross‑segment continuation.
By Yin Yi, Haiping Ma, Siyu Song, Ziwen Wang, Shangshang Yang
The paper introduces Process-aware Language Cognitive Diagnosis (PLCD), a framework that replaces traditional ID-based embeddings in Cognitive Diagnosis Models with language-derived structures and response records. PLCD employs large language models to build concept schemas and cognitive process graphs, and uses a Language-to-Cognition Mapper with DA-MoE experts and contrastive learning to map textual evidence into a unified cognitive space. Experiments demonstrate that PLCD outperforms conventional baselines in student performance prediction and shows strong cognitive transfer, improving cold-start robustness and cognitive grounding.
By Minghang Liu, Yuanzhuo Wang, Qiang Qiu, Huawei Shen, Xueqi Cheng
arXiv:2607. 13103v1 Announce Type: cross Abstract: Knowledge tracing (KT) aims to predict students' future performance by modeling their evolving knowledge states from historical interactions.
By Duantengchuan Li, Yingqian Bi, Jinsong Chen, Rui Zhang, Mingwen Tong
Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary. We propose FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction that supports cross-domain transfer: the inference graph may contain entirely unseen entities, relation names, and timestamps drawn from a different domain.
arXiv:2608. 10668v1 Announce Type: new Abstract: Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary.
By Jiaxin Pan, Mojtaba Nayyeri, Osama Mohammed, Daniel Hernandez, Rongchuan Zhang, Cheng Cheng, Steffen Staab