arXiv:2608. 08617v1 Announce Type: new Abstract: Group discussion-based teaching is widely used to foster collaborative learning, yet teachers in physical classrooms often struggle to simultaneously monitor multiple groups and quickly diagnose a target group before intervening.
By Yiping Sun, Ziyao Kang, Wei Zeng, Minli Wu, Jiazhi Xia
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
The paper introduces a computational framework that detects dynamic team‑process phases in collaborative virtual reality (VR) by analyzing timestamped dialogue. It uses late chunking, penalized Gaussian‑kernel change‑point detection, TF‑IDF, NMF, and a locally deployed large language model to identify semantic transitions and generate interpretable phase descriptions. The detected phases are aligned with interaction logs, demonstrating that transcript‑derived phases correspond to task‑action patterns and thus provide a transparent, transferable method for studying temporal changes in teamwork.
By Qing Huang, Jianing Zhang, Pooja Pol
The article discusses the growing use of large language models (LLMs) to assess student discourse at scale, noting that current validation methods—such as expert annotations and F1 scores—often ignore the contextual and cultural nuances of student language. It argues that these practices inadequately capture the experiences of racially and linguistically marginalized youth, and proposes re‑contextualizing classroom conversations and involving students as epistemic authorities. A case study with multilingual 8th‑grade math students demonstrates misalignments between student self‑interpretations and LLM outputs, underscoring the need for youth participation in validating LLM‑based measures.
By Liliana Santos-Deonizio, James Malamut, Ram\'on Mart\'inez, Dorottya Demszky
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
By Zhiqing Wang, Steven Dow
arXiv:2601. 19919v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is one of the most effective paradigms for compressing large-scale foundation models into deployable architectures.
By Junseok Lee, Nahun Kim, Sangyong Lee, Chang-Jae Chun