The paper introduces LT‑MKT, a new method for multi‑domain knowledge tracing that incorporates cognitive load and knowledge transfer. It constructs a multi‑domain hierarchical graph using textual information from questions and concepts, then explicitly models cross‑domain temporal and knowledge features to capture cognitive load effects. A knowledge transfer module further captures propagation of knowledge states within and across domains, leading to more accurate predictions of students’ future performance.
By Haotian Zhang, Shucun Wang, Jinze Wu, Liang Ding, Shuochen Liu, Zhenya Huang, Jing Sha, Shijin Wang, Qi Liu
arXiv:2606. 31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.
By Gaurab Baral, Aaditya Khanal, Yangyang Tao, Junxiu Zhou
The study evaluates how well pre‑trained models can classify the Bloom level of AI‑generated educational questions, a task that is crucial for ensuring pedagogical quality. Traditional machine‑learning models perform poorly on out‑of‑distribution data, whereas transformer and large‑language models achieve higher accuracy, especially after feature‑engineering techniques such as text splicing and appending learning objectives. Retraining the models yields the most significant performance gains across all datasets.
By Michael Lawrence Castanares, Princess Ventures, Allan Tan
arXiv:2605. 02906v3 Announce Type: replace Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention.
By Jingkai He, Pengfei Chen, Chenghui Wu, Shuang Liang, Ye Li, Gou Tan, Xidao Wen, Chuanfu Zhang, Fang Situ, Qi Zhou
arXiv:2609.36982v1 Announce Type: cross
Abstract: Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners...
By Zhiwei Yang, Jiahua Yang, Huiru Lin, Xing Chen, Quanlong Guan
EduDial is a large-scale multi-turn teacher‑student dialogue corpus covering 345 core knowledge points and 34,250 dialogue sessions, designed around Bloom’s taxonomy and ten questioning strategies such as situational, ZPD, and metacognitive questioning. The dataset includes differentiated teaching strategies for students at varying cognitive levels to provide targeted guidance. Using EduDial, the authors trained EduDial‑LLM 32B and introduced an 11‑dimensional evaluation framework that measures teaching quality and content quality, showing that most mainstream LLMs struggle with student‑centered teaching while EduDial‑LLM outperforms all baselines across all metrics.
By Shouang Wei, Min Zhang, Xin Lin, Bo Jiang, Zhongxiang Dai, Kun Kuang
Large language models (LLMs) achieve strong performance across many tasks, but their high computational cost limits deployment in resource-constrained environments. Knowledge Distillation (KD) offers a practical solution by transferring knowledge from a teacher model of a larger size to a smaller student model.
arXiv:2606. 07527v1 Announce Type: cross Abstract: The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL.
By Michael Hassid, Yossi Adi, Roy Schwartz
arXiv:2606. 13684v1 Announce Type: cross Abstract: Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent.
By Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani, Mohammadreza Tavakoli, G\'abor Kismih\'ok
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
By Selen Erkan, Bastian Boll, Kristian Kersting, Bj\"orn Deiseroth, Letitia Parcalabescu
arXiv:2510.13935v3 Announce Type: replace-cross
Abstract: The facts a language model stores are tied to its parameter count, so small models that fit on edge devices fail on expert problems, which ne...
By Kenan Alkiek, David Jurgens, Vinod Vydiswaran
arXiv:2608. 09351v1 Announce Type: cross Abstract: Test-time scaling improves LLM accuracy but multiplies inference cost, making the accuracy gained per unit of compute the metric that matters in deployment.
By Nikita Kozodoi, Zainab Afolabi, Jack Butler