The paper introduces an ability‑residual decoupled framework for affective cognitive diagnosis, which first isolates unmodeled cognitive residuals—such as item calibration bias, concept bias, and student‑concept deviations—using student, item, concept, student‑concept, and low‑rank student‑item components. It then applies an affective module that modulates guess/slip effects, with a Q‑matrix‑constrained concept residual attention mechanism to aggregate only item‑relevant concept residuals. Experiments on multiple datasets and backbones demonstrate improved response prediction and better affect alignment, while ablation and analysis studies show that the residual modeling reduces cognitive contamination in the affective branch and enhances robustness and accuracy.
By Boyuan Zhao, Meng Ye
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. 01278v1 Announce Type: new Abstract: The research proposes a multilayer Q-matrix-embedded neural network for cognitive diagnosis (M-QCDNet), which integrates the structural interpretability of cognitive diagnostic models (CDMs) with the deep learning neural network (NN).
By Yiyao Yang
arXiv:2608. 15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities.
By Alona Strugatski, Licol Zeinfeld, Giora Alexandron
arXiv:2603. 13761v2 Announce Type: replace Abstract: Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance.
By Amogh Inamdar, Zhenwei Tang, Ashton Anderson, Richard Zemel
arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.
By Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, Dawei Zhou
arXiv:2606. 18257v1 Announce Type: cross Abstract: While LLMs show promise in automating educational content creation, their ability to generate questions that stimulate higher-order thinking remains understudied.
By Xiaolong Wang, Zhe Zhao, Song Lai, Chaoli Zhang, Zijie Geng, Yu Tong, Ye Wei, Qingsong Wen
arXiv:2509.05346v3 Announce Type: replace
Abstract: While large language models (LLMs) are increasingly being adopted to support personalized learning, there remains limited understanding of how thei...
By Bo Yuan, Jiazi Hu
arXiv:2606. 05983v1 Announce Type: new Abstract: Generative AI makes answers easy and understanding hard, and uncritical use invites cognitive offloading.
By Alexander Apartsin, Yehudit Aperstein
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.
By Christopher Ormerod
arXiv:2504.02323v5 Announce Type: replace
Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored variou...
By Clayton Cohn, Ashwin T S, Naveeduddin Mohammed, Gautam Biswas
arXiv:2606. 15349v1 Announce Type: cross Abstract: Standardized examinations are typically treated as uniform syllabus coverage problems.
By Joy Bose, Om Thomas